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Zhang, Yuqing. "Fixed-time algebraic distributed state estimation for linear systems." Electronic Thesis or Diss., Bourges, INSA Centre Val de Loire, 2025. http://www.theses.fr/2025ISAB0001.
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Au cours des dernières décennies, le déploiement massif de capteurs embarqués en réseau dotés des capacités de communication dans des systèmes à grande échelle a suscité un intérêt croissant de la part des chercheurs dans le domaine de l’estimation distribuée. Cette thèse vise à développer une méthode d’estimation d’état distribuée algébrique à temps fixe pour les systèmes linéaires à temps variant d’ordre entier et les systèmes linéaires à temps invariant d’ordre fractionnaire dans des environnements bruités, en concevant un ensemble d’estimateurs locaux d’ordre réduit au niveau des capteurs en réseau.Pour ce faire, nous introduisons d’abord un schéma d’estimation distribuée en définissant un ensemble denoeuds récupérés à chaque noeud de capteur, basé sur un graphe dirigé plus relâché que celui qui est fortement connecté. En utilisant cet ensemble récupéré, nous construisons une transformation inversible pour la décomposition d’observabilité afin d’identifier le sous-système local observable de chaque noeud. De plus, cette transformation permet une représentation distribuée de l’état entier du système à chaque noeud sous forme de combinaison linéaire de son propre état local observable et de ceux des noeuds de son ensemble récupéré. Cela garantit que chaque noeud peut atteindre l’estimation distribuée d’état, à condition que les estimations des états locaux observables soient assurées. En conséquence, ce schéma distribué se concentre sur l’estimation des états locaux observables, permettant une estimation distribuée à travers le réseau de capteurs.En nous appuyant sur cette base, afin de traiter l’estimation algébrique à temps fixe pour chaque sous-système local observable identifié, différentes méthodes d’estimation à fonctions modulatrices sont explorées pour établir des formules algébriques indépendantes des conditions initiales, les rendant efficaces en tant qu’estimateurs locaux de ordre réduit à temps fixe. Pour les systèmes linéaires à temps variant d’ordre entier, la transformation utilisée pour développer le schéma d’estimation distribuée aboutit à une forme normale linéaire partiellement observable à temps variant. La méthode des fonctions modulatrices généralisées est ensuite appliquée pour estimer chaque état local observable à travers des formules intégrales algébriques des sorties du système et de leurs dérivées. Pour les systèmes linéaires à temps invariant d’ordre fractionnaire, une autre transformation est utilisée pour convertir chaque sous-système local observable identifié sous une forme normale observable d’ordre fractionnaire, permettant l’application de la méthode d’estimation à fonctions modulatrices généralisées d’ordre fractionnaire. Cette méthode calcule directement des formules intégrales algébriques pour les variables pseudo-état locales observables.Ensuite, en combinant ces formules algébriques avec la représentation distribuée dérivée, nous réalisons l’estimation d’état distribuée algébrique à temps fixe pour les systèmes étudiés. De plus, une analyse d’erreur est réalisée pour démontrer la robustesse de l’estimateur distribué conçu en présence de bruits continus de processus et de mesure, ainsi que de bruits discrets de mesure. Enfin, plusieurs exemples de simulation sont fournis pour valider l’efficacité du schéma d’estimation distribuée proposéIn recent decades, the widespread deployment of networked embedded sensors with communication capabilities in large-scale systems has drawn significant attentions fromresearchers to the field of distributed estimation. This thesis aims to develop a fixed-time algebraic distributed state estimation method for both integer-order linear time-varying systems and fractional-order linear-invariant systems in noisy environments, by designing a set of reduced-order local estimators at the networked sensors.To achieve this, we first introduce a distributed estimation scheme by defining a recovered node set at each sensor node, based on a digraph assumption that is more relaxed than the strongly connected one. Using this recovered set, we construct an invertible transformation for the observability decomposition to identify each node’s local observable subsystem. Additionally, this transformation allows for a distributed representation of the entire system state at each node by a linear combination of its own local observable state and those of the nodes in its recovered set. This ensures that each node can achieve the distributed state estimation, provided that the estimations for the set of local observable states are ensured. As a result, this distributed scheme focuses on estimating the local observable states, enabling distributed estimation across the sensor network.Building on this foundation, to address the fixed-time algebraic state estimation for each identified local observable subsystem, different modulating functions estimation methods are investigated to derive the initial-condition-independent algebraic formulas, making them effective as reduced-order local fixed-time estimators. For integer-order linear time-varying systems, the transformation used in developing distributed estimation scheme yields a linear time-varying partial observable normal form. The generalized modulating functions method is then applied to estimate each local observable state through algebraic integral formulas of system outputs and their derivatives. For fractional-order linear-invariant systems, another transformation is used to convert each identified local observable subsystem into a fractional-order observable normal form, allowing for the application of the fractional-order generalized modulating functions estimation method. This method directly computes algebraic integral formulas for local observable pseudo-state variables.Subsequently, by combining these algebraic formulas with the derived distributed representation, we achieve the fixed-time algebraic distributed state estimation for the studied systems. Additionally, an error analysis is conducted to demonstrate the robustness of the designed distributed estimator in the presence of both continuous process and measurement noises, as well as discrete measurement noises. Finally, several simulation examples are provided to validate the effectiveness of the proposed distributed estimation scheme
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Copeland, Andrew David 1978. "Robust motion estimation in the presence of fixed pattern noise." Thesis, Massachusetts Institute of Technology, 2003. http://hdl.handle.net/1721.1/87395.
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Thesis (M.Eng.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2003.Includes bibliographical references (p. 41-42).
by Andrew David Copeland.
M.Eng.
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Kwan, Tan Hwee. "Robust estimation for structural time series models." Thesis, London School of Economics and Political Science (University of London), 1990. http://etheses.lse.ac.uk/2809/.
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This thesis aims at developing robust methods of estimation in order to draw valid inference from contaminated time series. We concentrate on additive and innovation outliers in structural time series models using a state space representation. The parameters of interest are the state, hyperparameters and coefficients of explanatory variables. Three main contributions evolve from the research. Firstly, a filter named the approximate Gaussian sum filter is proposed to cope with noisy disturbances in both the transition and measurement equations. Secondly, the Kalman filter is robustified by carrying over the M-estimation of scale for i.i.d observations to time-dependent data. Thirdly, robust regression techniques are implemented to modify the generalised least squares transformation procedure to deal with explanatory variables in time series models. All the above procedures are tested against standard non-robust estimation methods for time series by means of simulations. Two real examples are also included.
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Sinha, Sanjoy Kumar. "Some aspects of robust estimation in time series analysis." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 2000. http://www.collectionscanada.ca/obj/s4/f2/dsk2/ftp03/NQ57354.pdf.
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Zheng, Xueying, and 郑雪莹. "Robust joint mean-covariance model selection and time-varying correlation structure estimation for dependent data." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 2013. http://hub.hku.hk/bib/B50899703.
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In longitudinal and spatio-temporal data analysis, repeated measurements from a subject can be either regional- or temporal-dependent. The correct specification of the within-subject covariance matrix cultivates an efficient estimation for mean regression coefficients.
In this thesis, robust estimation for the mean and covariance jointly for the regression model of longitudinal data within the framework of generalized estimating equations (GEE) is developed. The proposed approach integrates the robust method and joint mean-covariance regression modeling. Robust generalized estimating equations using bounded scores and leverage-based weights are employed for the mean and covariance to achieve robustness against outliers. The resulting estimators are shown to be consistent and asymptotically normally distributed.
Robust variable selection method in a joint mean and covariance model is considered, by proposing a set of penalized robust generalized estimating equations to estimate simultaneously the mean regression coefficients, the generalized autoregressive coefficients and innovation variances introduced by the modified Cholesky decomposition. The set of estimating equations select important covariate variables in both mean and covariance models together with the estimating procedure. Under some regularity conditions, the oracle property of the proposed robust variable selection method is developed. For these two robust joint mean and covariance models, simulation studies and a hormone data set analysis are carried out to assess and illustrate the small sample performance, which show that the proposed methods perform favorably by combining the robustifying and penalized estimating techniques together in the joint mean and covariance model.
Capturing dynamic change of time-varying correlation structure is both interesting and scientifically important in spatio-temporal data analysis. The time-varying empirical estimator of the spatial correlation matrix is approximated by groups of selected basis matrices which represent substructures of the correlation matrix. After projecting the correlation structure matrix onto the space spanned by basis matrices, varying-coefficient model selection and estimation for signals associated with relevant basis matrices are incorporated. The unique feature of the proposed model and estimation is that time-dependent local region signals can be detected by the proposed penalized objective function. In theory, model selection consistency on detecting local signals is provided. The proposed method is illustrated through simulation studies and a functional magnetic resonance imaging (fMRI) data set from an attention deficit hyperactivity disorder (ADHD) study.published_or_final_version
Statistics and Actuarial Science
Doctoral
Doctor of Philosophy
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Kovac, Arne. "Wavelet thresholding for unequally time-spaced data." Thesis, University of Bristol, 1999. http://hdl.handle.net/1983/2088715a-7792-4032-bb76-83e3b0389b94.
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Skoglund, Johan. "Robust Real-Time Estimation of Region Displacements in Video Sequences." Licentiate thesis, Linköping : Department of Electrical Engineering, Linköpings universitet, 2007. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-8006.
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LaMaire, Richard O. "Robust time and frequency domain estimation methods in adaptive control." Thesis, Massachusetts Institute of Technology, 1987. http://hdl.handle.net/1721.1/14795.
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Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 1987.MICROFICHE COPY AVAILABLE IN ARCHIVES AND ENGINEERING.
Supported, in part, by the NASA Ames & Langley Research Centers, the Office of Naval Research, and the National Science Foundation.
Bibliography: v. 2, leaves 334-337.
by Richard Orville LaMaire.
Ph.D.
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Staerman, Guillaume. "Functional anomaly detection and robust estimation." Electronic Thesis or Diss., Institut polytechnique de Paris, 2022. http://www.theses.fr/2022IPPAT021.
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L’engouement pour l’apprentissage automatique s’étend à presque tous les domaines comme l’énergie, la médecine ou la finance. L’omniprésence des capteurs met à disposition de plus en plus de données avec une granularité toujours plus fine. Une abondance de nouvelles applications telles que la surveillance d’infrastructures complexes comme les avions ou les réseaux d’énergie, ainsi que la disponibilité d’échantillons de données massives, potentiellement corrompues, ont mis la pression sur la communauté scientifique pour développer de nouvelles méthodes et algorithmes d’apprentissage automatique fiables. Le travail présenté dans cette thèse s’inscrit dans cette ligne de recherche et se concentre autour de deux axes : la détection non-supervisée d’anomalies fonctionnelles et l’apprentissage robuste, tant du point de vue pratique que théorique.La première partie de cette thèse est consacrée au développement d’algorithmes efficaces de détection d’anomalies dans le cadre fonctionnel. Plus précisément, nous introduisons Functional Isolation Forest (FIF), un algorithme basé sur le partitionnement aléatoire de l’espace fonctionnel de manière flexible afin d’isoler progressivement les fonctions les unes des autres. Nous proposons également une nouvelle notion de profondeur fonctionnelle basée sur l’aire de l’enveloppe convexe des courbes échantillonnées, capturant de manière naturelle les écarts graduels de centralité. Les problèmes d’estimation et de calcul sont abordés et diverses expériences numériques fournissent des preuves empiriques de la pertinence des approches proposées. Enfin, afin de fournir des recommandations pratiques, la performance des récentes techniques de détection d’anomalies fonctionnelles est évaluée sur deux ensembles de données réelles liés à la surveillance des hélicoptères en vol et à la spectrométrie des matériaux de construction.La deuxième partie est consacrée à la conception et à l’analyse de plusieurs approches statistiques, potentiellement robustes, mêlant la profondeur de données et les estimateurs robustes de la moyenne. La distance de Wasserstein est une métrique populaire résultant d’un coût de transport entre deux distributions de probabilité et permettant de mesurer la similitude de ces dernières. Bien que cette dernière ait montré des résultats prometteurs dans de nombreuses applications d’apprentissage automatique, elle souffre d’une grande sensibilité aux valeurs aberrantes. Nous étudions donc comment tirer partie des estimateurs de la médiane des moyennes (MoM) pour renforcer l’estimation de la distance de Wasserstein avec des garanties théoriques. Par la suite, nous introduisons une nouvelle fonction de profondeur statistique dénommée Affine-Invariante Integrated Rank-Weighted (AI-IRW). Au-delà de l’analyse théorique effectuée, des résultats numériques sont présentés, confirmant la pertinence de cette profondeur. Les sur-ensembles de niveau des profondeurs statistiques donnent lieu à une extension possible des fonctions quantiles aux espaces multivariés. Nous proposons une nouvelle mesure de similarité entre deux distributions de probabilité. Elle repose sur la moyenne de la distance de Hausdorff entre les régions quantiles, induites par les profondeur de données, de chaque distribution. Nous montrons qu’elle hérite des propriétés intéressantes des profondeurs de données telles que la robustesse ou l’interprétabilité. Tous les algorithmes développés dans cette thèse sont accessible en ligneEnthusiasm for Machine Learning is spreading to nearly all fields such as transportation, energy, medicine, banking or insurance as the ubiquity of sensors through IoT makes more and more data at disposal with an ever finer granularity. The abundance of new applications for monitoring of complex infrastructures (e.g. aircrafts, energy networks) together with the availability of massive data samples has put pressure on the scientific community to develop new reliable Machine-Learning methods and algorithms. The work presented in this thesis focuses around two axes: unsupervised functional anomaly detection and robust learning, both from practical and theoretical perspectives.The first part of this dissertation is dedicated to the development of efficient functional anomaly detection approaches. More precisely, we introduce Functional Isolation Forest (FIF), an algorithm based on randomly splitting the functional space in a flexible manner in order to progressively isolate specific function types. Also, we propose the novel notion of functional depth based on the area of the convex hull of sampled curves, capturing gradual departures from centrality, even beyond the envelope of the data, in a natural fashion. Estimation and computational issues are addressed and various numerical experiments provide empirical evidence of the relevance of the approaches proposed. In order to provide recommendation guidance for practitioners, the performance of recent functional anomaly detection techniques is evaluated using two real-world data sets related to the monitoring of helicopters in flight and to the spectrometry of construction materials.The second part describes the design and analysis of several robust statistical approaches relying on robust mean estimation and statistical data depth. The Wasserstein distance is a popular metric between probability distributions based on optimal transport. Although the latter has shown promising results in many Machine Learning applications, it suffers from a high sensitivity to outliers. To that end, we investigate how to leverage Medians-of-Means (MoM) estimators to robustify the estimation of Wasserstein distance with provable guarantees. Thereafter, a new statistical depth function, the Affine-Invariant Integrated Rank-Weighted (AI-IRW) depth is introduced. Beyond the theoretical analysis carried out, numerical results are presented, providing strong empirical confirmation of the relevance of the depth function proposed. The upper-level sets of statistical depths—the depth-trimmed regions—give rise to a definition of multivariate quantiles. We propose a new discrepancy measure between probability distributions that relies on the average of the Hausdorff distance between the depth-based quantile regions w.r.t. each distribution and demonstrate that it benefits from attractive properties of data depths such as robustness or interpretability. All algorithms developed in this thesis are open-sourced and available online
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Chapman, Michael Addison. "Adaptation and Installation of a Robust State Estimation Package in the Eef Utility." Thesis, Virginia Tech, 1999. http://hdl.handle.net/10919/31432.
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Robust estimation methods have been successfully applied to the problem of power system state estimation in a real-time environment. The Schweppe-type GM-estimator with the Huber psi-function (SHGM) has been fully installed in conjunction with a topology processor in the EEF utility, headquartered in Fribourg, Switzerland.
Some basic concepts of maximum likelihood estimation and robust analysis are reviewed, and applied to the development of the SHGM-estimator. The algorithms used by the topology processor and state estimator are presented, and the superior performance of the SHGM-estimator over the classic weighted least squares estimator is demonstrated on the EEF network. The measurement configuration of the EEF network has been evaluated, and suggestions for its reinforcement have been proposed.Master of Science
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Nielsen, Jerel Bendt. "Robust Visual-Inertial Navigation and Control of Fixed-Wing and Multirotor Aircraft." BYU ScholarsArchive, 2019. https://scholarsarchive.byu.edu/etd/7584.
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With the increased performance and reduced cost of cameras, the robotics community has taken great interest in estimation and control algorithms that fuse camera data with other sensor data.In response to this interest, this dissertation investigates the algorithms needed for robust guidance, navigation, and control of fixed-wing and multirotor aircraft applied to target estimation and circumnavigation.This work begins with the development of a method to estimate target position relative to static landmarks, deriving and using a state-of-the-art EKF that estimates static landmarks in its state.Following this estimator, improvements are made to a nonlinear observer solving part of the SLAM problem.These improvements include a moving origin process to keep the coordinate origin within the camera field of view and a sliding window iteration algorithm to drastically improve convergence speed of the observer.Next, observers to directly estimate relative target position are created with a circumnavigation guidance law for a multirotor aircraft.Taking a look at fixed-wing aircraft, a state-dependent LQR controller with inputs based on vector fields is developed, in addition to an EKF derived from error state and Lie group theory to estimate aircraft state and inertial wind velocity.The robustness of this controller/estimator combination is demonstrated through Monte Carlo simulations.Next, the accuracy, robustness, and consistency of a state-of-the-art EKF are improved for multirotors by augmenting the filter with a drag coefficient, partial updates, and keyframe resets.Monte Carlo simulations demonstrate the improved accuracy and consistency of the augmented filter.Lastly, a visual-inertial EKF using image coordinates is derived, as well as an offline calibration tool to estimate the transforms needed for accurate, visual-inertial estimation algorithms.The imaged-based EKF and calibrator are also shown to be robust under various conditions through numerical simulation.
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Kallapur, Abhijit Aerospace Civil & Mechanical Engineering Australian Defence Force Academy UNSW. "A discrete-time robust extended kalman filter for estimation of nonlinear uncertain systems." Publisher:University of New South Wales - Australian Defence Force Academy. Information Technology & Electrical Engineering, 2009. http://handle.unsw.edu.au/1959.4/44095.
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This thesis provides a novel approach to the problem of state estimation for discrete-time nonlinear systems in the presence of large model uncertainties. Though classical nonlinear Kalman filters such as the extended Kalman filter (EKF) can handle uncertainties by increasing the value of noise covariances, this is only applicable to systems with small uncertainties. To this end, a discretetime robust extended Kalman filter (REKF) is formulated and applied to examples from the fields of aerospace engineering and signal processing with an emphasis on attitude estimation for small unmanned aerial vehicles (UAVs) and image processing under the influence of atmospheric turbulence. The robust filter is an approximate set-valued state estimator where the Riccati and filter equations are obtained as an approximate solution to a reverse-time optimal control problem defining the set-valued state estimator. The advantages of the REKF over the classical EKF are investigated for examples from the fields aerospace engineering and signal processing where large model uncertainties are introduced. In the case of small UAVs, an alternative attitude estimation algorithm based on the REKF is proposed in the event of gyroscopic failure and the inability of the vehicle to carry redundant sensors due to limited payload capabilities. In the case of image reconstruction under atmospheric turbulence, a robust pixel-wandering (random shifts) scheme is proposed to aid the process of image reconstruction. Also, problems pertaining to platform vibration analysis for aerospace vehicles and a frequency demodulation process in the presence of channel-induced uncertainties is also discussed.
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Tjaden, Henning [Verfasser]. "Robust Monocular Pose Estimation of Rigid 3D Objects in Real-Time / Henning Tjaden." Mainz : Universitätsbibliothek Mainz, 2019. http://d-nb.info/1175913200/34.
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Strange, Andrew Darren. "Robust thin layer coal thickness estimation using ground penetrating radar." Thesis, Queensland University of Technology, 2007. https://eprints.qut.edu.au/16356/1/Andrew_Strange_Thesis.pdf.
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One of the most significant goals in coal mining technology research is the automation of underground coal mining machinery. A current challenge with automating underground coal mining machinery is measuring and maintaining a coal mining horizon. The coal mining horizon is the horizontal path the machinery follows through the undulating coal seam during the mining operation. A typical mining practice is to leave a thin remnant of coal unmined in order to maintain geological stability of the cutting face. If the remnant layer is too thick, resources are wasted as the unmined coal is permanently unrecoverable. If the remnant layer is too thin, the product is diluted by mining into the overburden and there is an increased risk of premature roof fall which increases danger.
The main challenge therefore is to develop a robust sensing method to estimate
the thickness of thin remant coal layers. This dissertation addresses this challenge by presenting a pattern recognition methodology to estimate thin remnant coal layer thickness using ground penetrating radar (GPR). The approach is based upon a novel feature vector, derived from the bispectrum, that is used to characterise the early-time segment of 1D GPR data.
The early-time segment is dominated by clutter inherent in GPR systems such as antenna crosstalk, ringdown and ground-bounce. It is common practice to either time-gate the signal, disregard the clutter by rendering the early-time segment unusable, or configure the GPR equipment to minimise the clutter effects which in turn reduces probing range. Disregarding the early-time signal essentially imposes a lower thickness limit on traditional GPR layer thickness estimators.
The challenges of estimating thin layer thickness is primarily due to these inherent clutter components. Traditional processing strategies attempt to minimise the clutter using pre-processing techniques such as the subtraction of a calibration signal. The proposed method, however, treats the clutter as a deterministic but unknown signal with additive noise. Hence the proposed approach utilises the energy from the clutter and monitors change in media from subtle changes in the signal shape.
Two complementary processing methods important to horizon sensing have been also proposed. These methods, near-surface interface detection and antenna height estimation, may be used as pre-validation tools to increase the robustness of the thickness estimation technique.
The proposed methods have been tested with synthetic data and validated with real data obtained using a low power 1.4 GHz GPR system and a testbed with known conditions. With the given test system, it is shown that the proposed thin layer thickness estimator and near-surface interface detector outperform the traditional matched filter based processing methods for layers less than 5 cm in thickness. It is also shown that the proposed antenna height estimator outperforms the traditional height estimator for heights less than 7 cm.
These new methods provide a means for reliably extending layer thickness estimation to the thin layer case where traditional approaches are known to fail.
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Strange, Andrew Darren. "Robust thin layer coal thickness estimation using ground penetrating radar." Queensland University of Technology, 2007. http://eprints.qut.edu.au/16356/.
Full textAbstract:
One of the most significant goals in coal mining technology research is the automation of underground coal mining machinery. A current challenge with automating underground coal mining machinery is measuring and maintaining a coal mining horizon. The coal mining horizon is the horizontal path the machinery follows through the undulating coal seam during the mining operation. A typical mining practice is to leave a thin remnant of coal unmined in order to maintain geological stability of the cutting face. If the remnant layer is too thick, resources are wasted as the unmined coal is permanently unrecoverable. If the remnant layer is too thin, the product is diluted by mining into the overburden and there is an increased risk of premature roof fall which increases danger. The main challenge therefore is to develop a robust sensing method to estimate the thickness of thin remant coal layers. This dissertation addresses this challenge by presenting a pattern recognition methodology to estimate thin remnant coal layer thickness using ground penetrating radar (GPR). The approach is based upon a novel feature vector, derived from the bispectrum, that is used to characterise the early-time segment of 1D GPR data. The early-time segment is dominated by clutter inherent in GPR systems such as antenna crosstalk, ringdown and ground-bounce. It is common practice to either time-gate the signal, disregard the clutter by rendering the early-time segment unusable, or configure the GPR equipment to minimise the clutter effects which in turn reduces probing range. Disregarding the early-time signal essentially imposes a lower thickness limit on traditional GPR layer thickness estimators. The challenges of estimating thin layer thickness is primarily due to these inherent clutter components. Traditional processing strategies attempt to minimise the clutter using pre-processing techniques such as the subtraction of a calibration signal. The proposed method, however, treats the clutter as a deterministic but unknown signal with additive noise. Hence the proposed approach utilises the energy from the clutter and monitors change in media from subtle changes in the signal shape. Two complementary processing methods important to horizon sensing have been also proposed. These methods, near-surface interface detection and antenna height estimation, may be used as pre-validation tools to increase the robustness of the thickness estimation technique. The proposed methods have been tested with synthetic data and validated with real data obtained using a low power 1.4 GHz GPR system and a testbed with known conditions. With the given test system, it is shown that the proposed thin layer thickness estimator and near-surface interface detector outperform the traditional matched filter based processing methods for layers less than 5 cm in thickness. It is also shown that the proposed antenna height estimator outperforms the traditional height estimator for heights less than 7 cm. These new methods provide a means for reliably extending layer thickness estimation to the thin layer case where traditional approaches are known to fail.
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Meneghel, Danilevicz Ian. "Robust linear mixed models, alternative methods to quantile regression for panel data, and adaptive LASSO quantile regression with fixed effects." Electronic Thesis or Diss., université Paris-Saclay, 2022. http://www.theses.fr/2022UPAST176.
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La thèse est constituée de trois chapitres. Le premier s'intéresse au lien entre l’exposition à la pollution de l’air et les affections respiratoires chez les enfants et les adolescents. La cohorte comprend 82 individus observés mensuellement pendant 6 mois. Nous proposons un modèle linéaire mixte robuste combiné à une analyse en composantes principales afin de gérer la multicolinéarité entre les covariables et l’impact des observations extrêmes sur les estimations. Le deuxième chapitre analyse des données de panel au moyen de modèles à effets fixes et utilisant différentes fonction de perte. Afin d'éviter que le nombre de paramètres augmente avec la taille de l'échantillon, nous pénalisons chaque méthode de régression par LASSO. Les propriétés asymptotiques de ces nouvelles techniques sont établies. Nous testons les performances des méthodes avec des données de panel économiques issues de l'OCDE. Le but recherché dans le troisième chapitre est de contraindre simultanément les constantes de régression individuelles et les covariables explicatives. Le LASSO adaptatif permet de réduire la dimensionnalité en assurant asymptotiquement la sélection du bon modèle. Nous testons la précision des méthodes proposées sur des données de cohorte de dimension modéréeThis thesis consists of three chapters on longitudinal data analysis. Linear mixed models are discussed, both random effects (where individual intercepts are interpreted as random variables) and fixed effects (where individual intercepts are considered unknown constants, i.e., they must be estimated). Furthermore, robust models (resistant to outliers) and efficient models (with low estimator variability) are proposed in the scope of repeated measures. The second part of the thesis is dedicated to quantile regression, which explores the full conditional distribution of an outcome given its predictors. It introduces a more general method for dealing with heteroscedastic variables and longitudinal data. The first chapter is motivated by evaluating the statistical association between air pollution exposure and children and adolescents' lung ability among six months. A robust linear mixed model combined with an equally robust principal component analysis is proposed to deal with multicollinearity between covariates and the impact of extreme observations on the estimates. Huber and Tukey loss functions (M-estimation examples) are considered to obtain more robust estimators than the least squared function usually used to estimate the parameters of linear mixed models. A finite sample size study is carried out in the case where the covariates follow linear time series models with or without additive outliers. The impact of time correlation and outliers on fixed effect parameter estimates in linear mixed models is investigated. In addition, weights are introduced to reduce the estimates' bias even more. The study of the real data revealed that the robust principal component analysis exhibits three principal components explaining more than 90% of the total variability. The second principal component, which corresponds to particles smaller than 10 microns, significantly affects respiratory capacity. In addition, biological indicators such as passive smoking have a negative and significant effect on children's lung ability. The second chapter analyses fixed effect panel data with three different loss functions. To avoid the number of parameters increases with the sample size, we propose to penalize each regression method with the least absolute shrinkage and selection operator (LASSO). The asymptotic properties of two of these new techniques are established. A Monte Carlo study is performed for homoscedastic and heteroscedastic models. Although the model is more challenging to estimate in the heteroscedastic case for most statistical methods, the proposed methods perform well in both scenarios. This confirms that the proposed quantile regression methods are robust to heteroscedasticity. Their performance is tested on economic panel data from the Organisation for Economic Cooperation and Development (OECD). The objective of the third chapter is to simultaneously restrict the number of individual regression constants and explanatory covariates. In addition to the LASSO, an adaptive LASSO is proposed, which enjoys oracle proprieties, i.e., it owns the asymptotic selection of the true model if it exists, and it has the classical asymptotic normality property. Monte Carlo simulations are performed in the case of low dimensionality (much more observations than parameters) and in the case of moderate dimensionality (equivalent number of observations and parameters). In both cases, the adaptive method performs much better than the non-adaptive methods. Finally, we apply our methodology to a cohort dataset of moderate dimensionality. For each chapter, open-source software is written, which is available to the scientific community
Esta tese consiste em três capítulos sobre análise de dados longitudinais. São discutidos modelos lineares mistos, tanto efeitos aleatórios (onde interseptos individuais são interpretados como variáveis aleatórias) quanto efeitos fixos (onde interseptos individuais são considerados constantes desconhecidas, ou seja, devem ser estimadas). Além disso, modelos robustos (resistentes a outliers) e modelos eficientes (com baixa variabilidade de estimadores) são propostos no âmbito de medidas repetidas. A segunda parte da tese é dedicada à regressão quantílica, que explora toda a distribuição condicional de uma variável resposta dado suas preditoras. Ela introduz um método mais geral para lidar com variáveis heterocedásticas e dados longitudinais. O primeiro capítulo é motivado pela avaliação da associação estatística entre a exposição à poluição do ar e a capacidade pulmonar de crianças e adolescentes durante um período de seis meses. Um modelo linear misto robusto combinado com uma análise de componentes principais igualmente robusta é proposto para lidar com a multicolinearidade entre covariáveis e o impacto de observações extremas sobre as estimativas. As funções de perda Huber e Tukey (exemplos de \textit{M-estimation}) são consideradas para obter estimadores mais robustos do que a função de mínimos quadrados geralmente usada para estimar os parâmetros de modelos lineares mistos. Um estudo de tamanho de amostra finito é realizado no caso em que as covariáveis seguem modelos de séries temporais lineares com ou sem outliers aditivos. É investigado o impacto da correlação temporal e outliers nas estimativas de parâmetros de efeito fixo em modelos lineares mistos. Além disso, foram introduzidos pesos para reduzir ainda mais o enviesamento das estimativas. Um estudo em dados reais revelou que a análise robusta dos componentes principais apresenta três componentes principais que explicam mais de 90% da variabilidade total. O segundo componente principal, que corresponde a partículas menores que 10 micrômetros, afeta significativamente a capacidade respiratória. Além disso, os indicadores biológicos como o tabagismo passivo têm um efeito negativo e significativo na capacidade pulmonar das crianças. O segundo capítulo analisa dados de painel com efeito fixo com três diferentes funções de perda. Para evitar que o número de parâmetros aumente com o tamanho da amostra, propomos penalizar cada método de regressão com least absolute shrinkage and selection operator (LASSO). As propriedades assimptóticas de duas dessas novas técnicas são estabelecidas. Um estudo de Monte Carlo é realizado para modelos homocedásticos e heterosecásticos. Embora o modelo seja mais difícil de estimar no caso heterocedástico para a maioria dos métodos estatísticos, os métodos propostos têm bom desempenho em ambos os cenários. Isto confirma que os métodos de regressão quantílica propostos são robustos à heterocedasticidade. Seu desempenho é testado nos dados do painel econômico da Organização para Cooperação e Desenvolvimento Econômico (OCDE). O objetivo do terceiro capítulo é restringir simultaneamente o número de constantes de regressão individuais e covariáveis explicativas. Além do LASSO, é proposto um LASSO adaptativo que permite a seleção assimptótica do modelo verdadeiro, se este existir, e que desfruta da propriedade de normalidade assimptótica clássica. As simulações de Monte Carlo são realizadas no caso de baixa dimensionalidade (muito mais observações do que parâmetros) e no caso de dimensionalidade moderada (número equivalente de observações e parâmetros). Em ambos os casos, o método adaptativo tem um desempenho muito melhor do que os métodos não adaptativos. Finalmente, aplicamos nossa metodologia em um conjunto de dados de coorte de dimensionalidade moderada. Para cada capítulo, um software de código aberto é escrito e colocado à disposição da comunidade científica
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Sohrabi, Maryam. "On Robust Asymptotic Theory of Unstable AR(p) Processes with Infinite Variance." Thesis, Université d'Ottawa / University of Ottawa, 2016. http://hdl.handle.net/10393/34280.
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In this thesis, we explore some asymptotic results in heavy-tailed theory. There are many empirical and compelling evidence in statistics that require modeling with heavy tailed observations.
This thesis is divided into three parts. First, we consider a robust estimation of the mean vector for a sequence of independent and identically distributed observations in the domain of attraction of a stable law with possibly different indices of stability between 1 and 2. The suggested estimator is asymptotically normal with unknown parameters. We apply an asymptotically valid bootstrap to construct a confidence region for the mean vector. Furthermore, a simulation study is performed to show that the estimation method is efficient for conducting inference about the mean vector for multivariate heavy-tailed observations.
In the second part, we present the asymptotic distribution of M-estimators for
parameters in an unstable AR(p) process. The innovations are assumed to be in
the domain of attraction of a stable law with index 0 < α ≤ 2. In particular,
when the model involves repeated unit roots or conjugate complex unit roots, M-
estimators have a higher asymptotic rate of convergence compared to the least square estimators. Moreover, we show that the asymptotic results can be written as Ito stochastic integrals.
Finally, the preceding methodologies lead to develop the asymptotic theory of M-estimators for parameters in unstable AR(p) processes with nonzero location parameter.
Similar to the preceding cases, we assume that the process is driven by innovations in the domain of attraction of a stable law with index 0 < α ≤ 2. In this thesis, for all models, we also cover the finite variance case (α = 2).
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Wittmann, Robert [Verfasser], Heinz [Akademischer Betreuer] [Gutachter] Ulbrich, and Boris [Gutachter] Lohmann. "Robust Walking Robots in Unknown Environments : Dynamic Models, State Estimation and Real-Time Trajectory Optimization / Robert Wittmann ; Gutachter: Boris Lohmann, Heinz Ulbrich ; Betreuer: Heinz Ulbrich." München : Universitätsbibliothek der TU München, 2017. http://d-nb.info/1145141412/34.
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Bazargani, Hamid. "Real-Time Recognition of Planar Targets on Mobile Devices. A Framework for Fast and Robust Homography Estimation." Thesis, Université d'Ottawa / University of Ottawa, 2014. http://hdl.handle.net/10393/31698.
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The present thesis is concerned with the problem of robust pose estimation for planar targets in the context of real-time mobile vision. As a consequence of this research, individual developments made in isolation by earlier researchers are here considered together.
Several adaptations to the existing algorithms are undertaken yielding a unified framework for robust pose estimation. This framework is specifically designed to meet the growing demand for fast and robust estimation on power-constrained platforms.
For robust recognition of targets at very low computational costs, we employ feature based methods which are based on local binary descriptors allowing fast feature matching at run-time. The matching set is then fed to a robust parameter estimation algorithm in order to obtain a reliable homography. On the basis of our experimental results, it can be concluded that reliable homography estimates can be obtained using a device-friendly implementation of the Gaussian Elimination algorithm. We also show in this thesis that our simplified approach can significantly improve the homography estimation step in a hypothesize-and-verify scheme. The author's attention is focused not only on developing fast algorithms for the recognition framework but also on the optimized implementation of such algorithms. Any other recognition framework would similarly benefit from our optimized implementation.
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Gibson, Scott Brian. "Improved Dynamic Modeling and Robust Control of Autonomous Underwater Vehicles." Diss., Virginia Tech, 2018. http://hdl.handle.net/10919/84468.
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In this dissertation, we seek to improve the dynamic modeling and control of autonomous underwater vehicles (AUVs). We address nonlinear hydrodynamic modeling, simplifying modeling assumptions, and robust control for AUVs. In the literature, various hydrodynamic models exist with varying model complexity and with no universally accepted model. We compare various hydrodynamic models traditionally employed to predict the motion of AUVs by estimating model coefficients using least-squares and adaptive identifier techniques. Additionally, we derive several dynamic models for an AUV employing varying sets of simplifying assumptions. We experimentally assess the efficacy of invoking typical assumptions to simplify the equations of motion.
For robust control design, we develop a procedure for designing robust attitude controllers based on loop-shaping ideas. We specifically address the challenge of adjusting the desired actuator bandwidth in a loop-shaping design framework. Finally, we present a novel receding horizon H-infinity control algorithm to improve the control of autonomous vehicle systems working in high-disturbance environments, employing a Markov jump linear system framework to model the stochastic and non-stationary disturbances experienced by the vehicle. Our main results include a new Bounded Real Lemma for stability analysis and an output feedback H-infinity control synthesis algorithm.
This work uses numerical simulations and extensive field trials of autonomous underwater vehicles to identify and verify dynamic models and to validate control algorithms developed herein.Ph. D.
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Hu, Nan. "A unified discrepancy-based approach for balancing efficiency and robustness in state-space modeling estimation, selection, and diagnosis." Diss., University of Iowa, 2016. https://ir.uiowa.edu/etd/2224.
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Due to its generality and flexibility, the state-space model has become one of the most popular models in modern time domain analysis for the description and prediction of time series data. The model is often used to characterize processes that can be conceptualized as "signal plus noise," where the realized series is viewed as the manifestation of a latent signal that has been corrupted by observation noise.
In the state-space framework, parameter estimation is generally accomplished by maximizing the innovations Gaussian log-likelihood. The maximum likelihood estimator (MLE) is efficient when the normality assumption is satisfied. However, in the presence of contamination, the MLE suffers from a lack of robustness. Basu, Harris, Hjort, and Jones (1998) introduced a discrepancy measure (BHHJ) with a non-negative tuning parameter that regulates the trade-off between robustness and efficiency. In this manuscript, we propose a new parameter estimation procedure based on the BHHJ discrepancy for fitting state-space models. As the tuning parameter is increased, the estimation procedure becomes more robust but less efficient. We investigate the performance of the procedure in an illustrative simulation study. In addition, we propose a numerical method to approximate the asymptotic variance of the estimator, and we provide an approach for choosing an appropriate tuning parameter in practice. We justify these procedures theoretically and investigate their efficacy in simulation studies.
Based on the proposed parameter estimation procedure, we then develop a new model selection criterion in the state-space framework. The traditional Akaike information criterion (AIC), where the goodness-of-fit is assessed by the empirical log-likelihood, is not robust to outliers. Our new criterion is comprised of a goodness-of-fit term based on the empirical BHHJ discrepancy, and a penalty term based on both the tuning parameter and the dimension of the candidate model. We present a comprehensive simulation study to investigate the performance of the new criterion. In instances where the time series data is contaminated, our proposed model selection criterion is shown to perform favorably relative to AIC.
Lastly, using the BHHJ discrepancy based on the chosen tuning parameter, we propose two versions of an influence diagnostic in the state-space framework. Specifically, our diagnostics help to identify cases that influence the recovery of the latent signal, thereby providing initial guidance and insight for further exploration. We illustrate the behavior of these measures in a simulation study.
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Steckenrider, John Josiah. "Simultaneous Estimation and Modeling of State-Space Systems Using Multi-Gaussian Belief Fusion." Diss., Virginia Tech, 2020. http://hdl.handle.net/10919/97583.
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This work describes a framework for simultaneous estimation and modeling (SEAM) of dynamic systems using non-Gaussian belief fusion by first presenting the relevant fundamental formulations, then building upon these formulations incrementally towards a more general and ubiquitous framework. Multi-Gaussian belief fusion (MBF) is introduced as a natural and effective method of fusing non-Gaussian probability distribution functions (PDFs) in arbitrary dimensions efficiently and with no loss of accuracy. Construction of some multi-Gaussian structures for potential use in MBF is addressed. Furthermore, recursive Bayesian estimation (RBE) is developed for linearized systems with uncertainty in model parameters, and a rudimentary motion model correction stage is introduced. A subsequent improvement to motion model correction for arbitrarily non-Gaussian belief is developed, followed by application to observation models. Finally, SEAM is generalized to fully nonlinear and non-Gaussian systems. Several parametric studies were performed on simulated experiments in order to assess the various dependencies of the SEAM framework and validate its effectiveness in both estimation and modeling. The results of these studies show that SEAM is capable of improving estimation when uncertainty is present in motion and observation models as compared to existing methods. Furthermore, uncertainty in model parameters is consistently reduced as these parameters are updated throughout the estimation process. SEAM and its constituents have potential uses in robotics, target tracking and localization, state estimation, and more.Doctor of Philosophy
The simultaneous estimation and modeling (SEAM) framework and its constituents described in this dissertation aim to improve estimation of signals where significant uncertainty would normally introduce error. Such signals could be electrical (e.g. voltages, currents, etc.), mechanical (e.g. accelerations, forces, etc.), or the like. Estimation is accomplished by addressing the problem probabilistically through information fusion. The proposed techniques not only improve state estimation, but also effectively "learn" about the system of interest in order to further refine estimation. Potential uses of such methods could be found in search-and-rescue robotics, robust control algorithms, and the like. The proposed framework is well-suited for any context where traditional estimation methods have difficulty handling heightened uncertainty.
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Breloy, Arnaud. "Algorithmes d’estimation et de détection en contexte hétérogène rang faible." Thesis, Université Paris-Saclay (ComUE), 2015. http://www.theses.fr/2015SACLN021/document.
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Une des finalités du traitement d’antenne est la détection et la localisation de cibles en milieu bruité. Dans la plupart des cas pratiques, comme par exemple le RADAR ou le SONAR actif, il faut estimer dans un premier temps les propriétés statistiques du bruit, et plus précisément sa matrice de covariance ; on dispose à cette fin de données secondaires supposées identiquement distribuées. Dans ce contexte, les hypothèses suivantes sont généralement formulées : bruit gaussien, données secondaires ne contenant que du bruit, et bien sûr matériels fonctionnant parfaitement. Il est toutefois connu aujourd’hui que le bruit en RADAR est de nature impulsive et que l’hypothèse Gaussienne est parfois mal adaptée. C’est pourquoi, depuis quelques années, le bruit et en particulier le fouillis de sol est modélisé par des processus elliptiques, et principalement des Spherically Invariant Random Vectors (SIRV). Dans ce nouveau cadre, la Sample Covariance Matrix (SCM) estimant classiquement la matrice de covariance du bruit entraîne des pertes de performances très importantes des détecteurs / estimateurs. Dans ce contexte non-gaussien, d’autres estimateurs de la matrice de covariance mieux adaptés à cette statistique du bruit ont été développés : la Matrice du Point Fixe (MPF) et les M-estimateurs.Parallèlement, dans un cadre où le bruit se décompose sous la forme d’une somme d’un fouillis rang faible et d’un bruit blanc, la matrice de covariance totale est structurée sous la forme rang faible plus identité. Cette information peut être utilisée dans le processus d'estimation afin de réduire le nombre de données nécessaires. De plus, il aussi est possible d'utiliser le projecteur orthogonal au sous espace fouillis à la place de la matrice de covariance ce qui nécessite moins de données secondaires et d’être aussi plus robuste aux données aberrantes. On calcule classiquement ce projecteur à partir d'un estimateur de la matrice de covariance. Néanmoins l'état de l'art ne présente pas d'estimateurs à la fois être robustes aux distributions hétérogènes, et rendant compte de la structure rang faible des données. C'est pourquoi ces travaux se focalisent sur le développement de nouveaux estimateurs (de covariance et de sous espace), directement adaptés au contexte considéré. Les contributions de cette thèse s'orientent donc autour de trois axes :- Nous présenterons tout d'abord un modèle statistique précis : celui de sources hétérogènes ayant une covariance rang faible noyées dans un bruit blanc gaussien. Ce modèle et est, par exemple, fortement justifié pour des applications de type radar. Il à cependant peu été étudié pour la problématique d'estimation de matrice de covariance. Nous dériverons donc l'expression du maximum de vraisemblance de la matrice de covariance pour ce contexte. Cette expression n'étant pas une forme close, nous développerons différents algorithmes pour tenter de l'atteindre efficacement.- Nous développons de nouveaux estimateurs directs de projecteur sur le sous espace fouillis, ne nécessitant pas un estimé de la matrice de covariance intermédiaire, adaptés au contexte considéré.- Nous étudierons les performances des estimateurs proposés et de l'état de l'art sur une application de Space Time Adaptative Processing (STAP) pour radar aéroporté, au travers de simulations et de données réellesOne purpose of array processing is the detection and location of a target in a noisy environment. In most cases (as RADAR or active SONAR), statistical properties of the noise, especially its covariance matrix, have to be estimated using i.i.d. samples. Within this context, several hypotheses are usually made: Gaussian distribution, training data containing only noise, perfect hardware. Nevertheless, it is well known that a Gaussian distribution doesn’t provide a good empirical fit to RADAR clutter data. That’s why noise is now modeled by elliptical process, mainly Spherically Invariant Random Vectors (SIRV). In this new context, the use of the SCM (Sample Covariance Matrix), a classical estimate of the covariance matrix, leads to a loss of performances of detectors/estimators. More efficient estimators have been developed, such as the Fixed Point Estimator and M-estimators.If the noise is modeled as a low-rank clutter plus white Gaussian noise, the total covariance matrix is structured as low rank plus identity. This information can be used in the estimation process to reduce the number of samples required to reach acceptable performance. Moreover, it is possible to estimate the basis vectors of the clutter-plus-noise orthogonal subspace rather than the total covariance matrix of the clutter, which requires less data and is more robust to outliers. The orthogonal projection to the clutter plus noise subspace is usually calculated from an estimatd of the covariance matrix. Nevertheless, the state of art does not provide estimators that are both robust to various distributions and low rank structured.In this Thesis, we therefore develop new estimators that are fitting the considered context, to fill this gap. The contributions are following three axes :- We present a precise statistical model : low rank heterogeneous sources embedded in a white Gaussian noise.We express the maximum likelihood estimator for this context.Since this estimator has no closed form, we develop several algorithms to reach it effitiently.- For the considered context, we develop direct clutter subspace estimators that are not requiring an intermediate Covariance Matrix estimate.- We study the performances of the proposed methods on a Space Time Adaptive Processing for airborne radar application. Tests are performed on both synthetic and real data
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Schick, Ä°rvin C. (Ä°rvin Cemil). "Robust recursive estimation of the state of a discrete-time stochastic linear dynamic system in the presence of heavy-tailed observation noise." Thesis, Massachusetts Institute of Technology, 1989. http://hdl.handle.net/1721.1/14323.
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Eckstein, Adric. "Development of Robust Correlation Algorithms for Image Velocimetry using Advanced Filtering." Thesis, Virginia Tech, 2007. http://hdl.handle.net/10919/36338.
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Digital Particle Image Velocimetry (DPIV) is a planar measurement technique to measure the velocity within a fluid by correlating the motion of flow tracers over a sequence of images recorded with a camera-laser system. Sophisticated digital processing algorithms are required to provide a high enough accuracy for quantitative DPIV results. This study explores the potential of a variety of cross-correlation filters to improve the accuracy and robustness of the DPIV estimation. These techniques incorporate the use of the Phase Transform (PHAT) Generalized Cross Correlation (GCC) filter applied to the image cross-correlation. The use of spatial windowing is subsequently examined and shown to be ideally suited for the use of phase correlation estimators, due to their invariance to the loss of correlation effects.
The Robust Phase Correlation (RPC) estimator is introduced, with the coupled use of the phase correlation and spatial windowing. The RPC estimator additionally incorporates the use of a spectral filter designed from an analytical decomposition of the DPIV Signal-to-Noise Ratio (SNR). This estimator is validated in a variety of artificial image simulations, the JPIV standard image project, and experimental images, which indicate reductions in error on the order of 50% when correlating low SNR images. Two variations of the RPC estimator are also introduced, the Gaussian Transformed Phase Correlation (GTPC): designed to optimize the subpixel interpolation, and the Spectral Phase Correlation (SPC): estimates the image shift directly from the phase content of the correlation. While these estimators are designed for DPIV, the methodology described here provides a universal framework for digital signal correlation analysis, which could be extended to a variety of other systems.Master of Science
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Preve, Daniel. "Essays on Time Series Analysis : With Applications to Financial Econometrics." Doctoral thesis, Uppsala University, Department of Information Science, 2008. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-8638.
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This doctoral thesis is comprised of four papers that all relate to the subject of Time Series Analysis.
The first paper of the thesis considers point estimation in a nonnegative, hence non-Gaussian, AR(1) model. The parameter estimation is carried out using a type of extreme value estimators (EVEs). A novel estimation method based on the EVEs is presented. The theoretical analysis is complemented with Monte Carlo simulation results and the paper is concluded by an empirical example.
The second paper extends the model of the first paper of the thesis and considers semiparametric, robust point estimation in a nonlinear nonnegative autoregression. The nonnegative AR(1) model of the first paper is extended in three important ways: First, we allow the errors to be serially correlated. Second, we allow for heteroskedasticity of unknown form. Third, we allow for a multi-variable mapping of previous observations. Once more, the EVEs used for parameter estimation are shown to be strongly consistent under very general conditions. The theoretical analysis is complemented with extensive Monte Carlo simulation studies that illustrate the asymptotic theory and indicate reasonable small sample properties of the proposed estimators.
In the third paper we construct a simple nonnegative time series model for realized volatility, use the results of the second paper to estimate the proposed model on S&P 500 monthly realized volatilities, and then use the estimated model to make one-month-ahead forecasts. The out-of-sample performance of the proposed model is evaluated against a number of standard models. Various tests and accuracy measures are utilized to evaluate the forecast performances. It is found that forecasts from the nonnegative model perform exceptionally well under the mean absolute error and the mean absolute percentage error forecast accuracy measures.
In the fourth and last paper of the thesis we construct a multivariate extension of the popular Diebold-Mariano test. Under the null hypothesis of equal predictive accuracy of three or more forecasting models, the proposed test statistic has an asymptotic Chi-squared distribution. To explore whether the behavior of the test in moderate-sized samples can be improved, we also provide a finite-sample correction. A small-scale Monte Carlo study indicates that the proposed test has reasonable size properties in large samples and that it benefits noticeably from the finite-sample correction, even in quite large samples. The paper is concluded by an empirical example that illustrates the practical use of the two tests.
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Johnson, Tomas. "Computer-aided Computation of Abelian integrals and Robust Normal Forms." Doctoral thesis, Uppsala universitet, Matematiska institutionen, 2009. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-107519.
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This PhD thesis consists of a summary and seven papers, where various applications of auto-validated computations are studied. In the first paper we describe a rigorous method to determine unknown parameters in a system of ordinary differential equations from measured data with known bounds on the noise of the measurements. Papers II, III, IV, and V are concerned with Abelian integrals. In Paper II, we construct an auto-validated algorithm to compute Abelian integrals. In Paper III we investigate, via an example, how one can use this algorithm to determine the possible configurations of limit cycles that can bifurcate from a given Hamiltonian vector field. In Paper IV we construct an example of a perturbation of degree five of a Hamiltonian vector field of degree five, with 27 limit cycles, and in Paper V we construct an example of a perturbation of degree seven of a Hamiltonian vector field of degree seven, with 53 limit cycles. These are new lower bounds for the maximum number of limit cycles that can bifurcate from a Hamiltonian vector field for those degrees. In Papers VI, and VII, we study a certain kind of normal form for real hyperbolic saddles, which is numerically robust. In Paper VI we describe an algorithm how to automatically compute these normal forms in the planar case. In Paper VII we use the properties of the normal form to compute local invariant manifolds in a neighbourhood of the saddle.
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Yin, Feng, Carsten Fritsche, Fredrik Gustafsson, and Abdelhak M. Zoubir. "TOA-Based Robust Wireless Geolocation and Cramér-Rao Lower Bound Analysis in Harsh LOS/NLOS Environments." Linköpings universitet, Reglerteknik, 2013. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-92694.
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We consider time-of-arrival based robust geolocation in harsh line-of-sight/non-line-of-sight environments. Herein, we assume the probability density function (PDF) of the measurement error to be completely unknown and develop an iterative algorithm for robust position estimation. The iterative algorithm alternates between a PDF estimation step, which approximates the exact measurement error PDF (albeit unknown) under the current parameter estimate via adaptive kernel density estimation, and a parameter estimation step, which resolves a position estimate from the approximate log-likelihood function via a quasi-Newton method. Unless the convergence condition is satisfied, the resolved position estimate is then used to refine the PDF estimation in the next iteration. We also present the best achievable geolocation accuracy in terms of the Cramér-Rao lower bound. Various simulations have been conducted in both real-world and simulated scenarios. When the number of received range measurements is large, the new proposed position estimator attains the performance of the maximum likelihood estimator (MLE). When the number of range measurements is small, it deviates from the MLE, but still outperforms several salient robust estimators in terms of geolocation accuracy, which comes at the cost of higher computational complexity.
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McPhee, Hamish. "Algorithme d'échelle de temps autonome et robuste pour un essaim de nanosatellites." Electronic Thesis or Diss., Université de Toulouse (2023-....), 2024. http://www.theses.fr/2024TLSEP094.
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Un nouvel algorithme est proposé et validé pour générer une échelle de temps robuste. Prévu pour une utilisation dans un essaim de nanosatellites, l'Autonomous Time Scale using the Student's T-distribution (ATST) peut traiter les anomalies subies par les horloges et les liens inter-satellites dans un environnement hostile. Les types d'anomalies traités incluent les sauts de phase, les sauts de fréquence, un bruit de mesure élevé dans certains liens et les données manquantes. En prenant la moyenne pondérée des résidus contenus dans l'équation de l'échelle de temps de base (BTSE), la contribution des satellites avec des mesures anormales est réduite pour la génération de l'échelle de temps. Les poids attribués à chaque horloge sont basés sur l'hypothèse que les résidus suivent la loi de Student.La performance de l'algorithme ATST est équivalente à celle de l'algorithme AT1 oracle, qui est une version de l'échelle de temps AT1 avec la capacité de détecter parfaitement toutes les anomalies dans des données simulées. Bien que l'algorithme n'ait pas de méthode de détection explicite, l'ATST affiche toujours un niveau de robustesse comparable à celui d'un détecteur parfait. Cependant, l'ATST est conçu pour un essaim avec de nombreuses horloges de types homogènes et est limité par une complexité numérique élevée. De plus, les anomalies sont toutes traitées de la même manière sans distinction entre les différents types d'anomalies. Malgré ces limitations identifiées, le nouvel algorithme représente une contribution prometteuse dans le domaine des échelles de temps grâce à la robustesse atteinte.Une méthode de traitement des horloges ajoutées ou retirées de l'ensemble est également proposée dans cette thèse en conjonction avec l'ATST. Cette méthode préserve la continuité de phase et de fréquence de l'échelle de temps en attribuant un poids nul aux horloges pertinentes lorsque le nombre total d'horloges est modifié. Un estimateur des moindres carrés (Least Squares, LS) est présenté pour montrer comment les mesures des liens inter-satellites peuvent être traitées en amont pour réduire le bruit de mesure et en même temps remplacer les mesures manquantes. L'estimateur LS peut être utilisé avec une méthode de détection qui élimine les mesures anormales, puis l'estimateur LS remplace les mesures supprimées par les estimations correspondantes.Cette thèse examine également l'estimation optimale de l'estimateur du maximum de vraisemblance (MLE) pour les paramètres des lois de probabilités à queues lourdes : précisément la loi de Student et la loi des mélanges gaussiens. Les améliorations obtenues en supposant correctement ces lois par rapport à l'hypothèse de la loi gaussienne sont démontrées avec les bornes de Cramér-Rao mal spécifiées (MCRB). Le MCRB dérivé confirme que les lois à queues lourdes sont meilleures pour l'estimation de la moyenne en présence de valeurs aberrantes. L'estimation des paramètres des lois à queues lourdes nécessite au moins 25 horloges pour obtenir l'erreur minimale, c'est-à-dire que l'estimateur atteigne l'efficacité asymptotique. Cette méthodologie pourra nous aider à analyser d'autres types d'anomalies suivant des lois différentes.Des propositions pour des pistes de recherche futures incluent le traitement des limitations de l'algorithme ATST concernant les types et le nombre d'horloges. Une nouvelle moyenne pour attribuer les poids en utilisant le machine learning est envisageable grâce à la compréhension des résidus du BTSE. Les anomalies transitoires peuvent être mieux traitées par le machine learning ou même avec un estimateur robuste de la fréquence des horloges sur une fenêtre de données passées. Cela est intéressant à explorer et à comparer à l'algorithme ATST, qui est proposé pour des anomalies instantanéesA new robust time scale algorithm, the Autonomous Time scale using the Student's T-distribution (ATST), has been proposed and validated using simulated clock data. Designed for use in a nanosatellite swarm, ATST addresses phase jumps, frequency jumps, anomalous measurement noise, and missing data by making a weighted average of the residuals contained in the Basic Time Scale Equation (BTSE). The weights come from an estimator that assumes the BTSE residuals are modeled by a Student's t-distribution.Despite not detecting anomalies explicitly, the ATST algorithm performs similarly to a version of the AT1 time scale that detects anomalies perfectly in simulated data. However, ATST is best for homogeneous clock types, requires a high number of clocks, adds computational complexity, and cannot necessarily differentiate anomaly types. Despite these identified limitations the robustness achieved is a promising contribution to the field of time scale algorithms.The implementation of ATST includes a method that maintains phase and frequency continuity when clocks are removed or reintroduced into the ensemble by resetting appropriate clock weights to zero. A Least Squares (LS) estimator is also presented to pre-process inter-satellite measurements, reducing noise and estimating missing data. The LS estimator is also compatible with anomaly detection which removes anomalous inter-satellite measurements because it can replace the removed measurements with their estimates.The thesis also explores optimal estimation of parameters of two heavy-tailed distributions: the Student's t and Bimodal Gaussian mixture. The Misspecified Cramér Rao Bound (MCRB) confirms that assuming heavy-tailed distributions handles outliers better compared to assuming a Gaussian distribution. We also observe that at least 25 clocks are required for asymptotic efficiency when estimating the mean of the clock residuals. The methodology also aids in analyzing other anomaly types fitting different distributions.Future research proposals include addressing ATST's limitations with diverse clock types, mitigating performance loss with fewer clocks, and exploring robust time scale generation using machine learning to weight BTSE residuals. Transient anomalies can be targeted using machine learning or even a similar method of robust estimation of clock frequencies over a window of past data. This is interesting to research and compare to the ATST algorithm that is instead proposed for instantaneous anomalies
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Jesus, Gildson Queiroz de. "Filtragem robusta recursiva para sistemas lineares a tempo discreto com parâmetros sujeitos a saltos Markovianos." Universidade de São Paulo, 2011. http://www.teses.usp.br/teses/disponiveis/18/18153/tde-03102011-091822/.
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Este trabalho trata de filtragem robusta para sistemas lineares sujeitos a saltos Markovianos discretos no tempo. Serão desenvolvidas estimativas preditoras e filtradas baseadas em algoritmos recursivos que são úteis para aplicações em tempo real. Serão desenvolvidas duas classes de filtros robustos, uma baseada em uma estratégia do tipo H \'INFINITO\' e a outra baseada no método dos mínimos quadrados regularizados robustos. Além disso, serão desenvolvidos filtros na forma de informação e seus respectivos algoritmos array para estimar esse tipo de sistema. Neste trabalho assume-se que os parâmetros de saltos do sistema Markoviano não são acessíveis.This work deals with the problem of robust state estimation for discrete-time uncertain linear systems subject to Markovian jumps. Predicted and filtered estimates are developed based on recursive algorithms which are useful in on-line applications. We develop two classes of filters, the first one is based on a H \'INFINITO\' approach and the second one is based on a robust regularized leastsquare method. Moreover, we develop information filter and their respective array algorithms to estimate this kind of system. We assume that the jump parameters of the Markovian system are not acessible.
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Lopez, Ramirez Francisco. "Control and estimation in finite-time and in fixed-time via implicit Lyapunov functions." Thesis, Lille 1, 2018. http://www.theses.fr/2018LIL1I063/document.
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Dans ce travail, on montre des nouveaux résultats pour l’analyse et la synthèse des systèmes stables en temps fini et fixe. Ce genre des systèmes convergent exactement à un point d’équilibre dans une quantité du temps qui est fini et, dans le cas de systèmes stables en temps fixe, dans un temps maximal constant qui ne dépend pas des conditions initiales du système.Les chapitres 2 et 3 portent sur des résultats d’analyse ; ce premier present des conditions nécessaires et suffisants pour la stabilité en temps fixe des systèmes autonomes continues tandis que ce dernier combine l’approche de la fonction implicite de Lyapunov avec des résultats de stabilisation ISS pour étudier la robustesse de ce genre de systèmes.Les chapitres 4 et 5 présentent des résultats pratiques liés á la procédure de synthèse des contrôleurs et des observateurs. Le chapitre 4 emploie la méthode de la fonction de Lyapunov implicite afin d’obtenir des observateurs convergents en temps fini et fixe pour les systèmes linéaires MIMO. Le chapitre 5 utilise des propriétés d’homogénéité et des fonctions de Lyapunov implicites pour synthétiser un contrôleur de sortie en temps fixe pour une chaîne d’intégrateurs. Les résultats obtenus ont été validés par des simulations numériques et le chapitre 4 contient des tests de performance sur un pendule rotatifThis work presents new results on analysis and synthesis of finite-time and fixed-time stable systems, a type of dynamical systems where exact convergence to an equilibrium point is guaranteed in a finite amount of time. In the case of fixed-time stable system, this is moreover achieved with an upper bound on the settling-time that does not depend on the system’s initial condition.Chapters 2 and 3 focus on theoretical contributions; the former presents necessary and sufficient conditions for fixed-time stability of continuous autonomous systems whereas the latter introduces a framework that gathers ISS Lyapunov functions, finite-time and fixed-time stability analysis and the implicit Lyapunov function approach in order to study and determine the robustness of this type of systems.Chapters 4 and 5 deal with more practical aspects, more precisely, the synthesis of finite-time and fixed-time controllers and observers. In Chapter 4, finite-time and fixed-time convergent observers are designed for linear MIMO systems using the implicit approach. In Chapter 5, homogeneity properties and the implicit approach are used to design a fixed-time output controller for the chain of integrators. The results obtained were verified by numerical simulations and Chapter 4 includes performance tests on a rotary pendulum
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Campos, José Carlos Teles. "Filtragem robusta para sistemas singulares discretos no tempo." Universidade de São Paulo, 2004. http://www.teses.usp.br/teses/disponiveis/18/18133/tde-07102015-150651/.
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Esta tese apresenta novos algoritmos que resolvem problemas de estimativas filtrada, suavizadora e preditora para sistemas singulares no tempo discreto usando apenas argumentos determinísticos. Cada capítulo aborda inicialmente as estimativas para o sistema nominal e em seguida, as versões robustas para o sistema com incertezas limitadas. Os resultados encontrados podem ser aplicados tanto em sistemas invariantes como variantes no tempo discreto, utilizando a mesma estrutura do filtro de Kalman. Nos últimos anos, uma quantidade significativa de trabalhos envolvendo estimativas singulares foi publicada enfocando apenas a estimativa filtrada sob a justificativa de que a estimativa preditora era de significativa complexidade quando modelada pelo método dos mínimos quadrados. Por este motivo, poucos trabalhos, como NIKOUKHAH et al. (1992) e ZHANG et al. (1998), deduziram a estimativa preditora. Este último artigo apresentou também um algoritmo para a estimativa suavizadora, mas usando o modelo de inovação ARMA. No entanto, até onde foi possível identificar, nenhum trabalho até agora resolveu o problema de estimativa robusta, considerando incertezas nos parâmetros, para sistemas singulares. Para a dedução das estimativas singulares robustas, esta tese tomou como base SAYED (2001), que deduz o filtro de Kalman robusto com incertezas limitadas utilizando uma abordagem determinística, o chamado filtro BDU. Os filtros robustos para sistemas singulares apresentados nesta tese, são mais abrangentes que os apresentados em SAYED (2001). Quando particularizados para o espaço de estados sem incertezas, todos os filtros se assemelham ao filtro de Kalman.New algorithms to optimal recursive filtering, smoothed and prediction for general time-invariant or time-variant descriptor systems are proposed in this thesis. The estimation problem is addressed as an optimal deterministic trajectory fitting. This problem is solved using exclusively deterministic arguments for systems with or without uncertainties. Kalman type recursive algorithms for robust filtered, predicted and smoothed estimations are derived. In the last years, many papers have paid attention to the estimation problems of linear singular systems. Unfortunately, all those works were concentrated only on the study of filtering problems, for nominal systems. The predicted and smoothed filters are more involved and were considered only by few works : NIKOUKHAH et al. (1992) and ZHANG et al. (1998) had proposed a unified approach for filtering, prediction and smoothing problems which were derived by using the projection formula and were calculated based on the ARMA innovation model, but they had not considered the uncertainties. In this thesis its applied for descriptor systems a robust procedure for usual state space systems developed by SAYED (2001), called BDU filter. It is obtained a robust descriptor Kalman type recursions for filtered, predicted and smoothed estimates. Considering the nominal state space, all descriptor filters developed in this work collapse to the Kalman filter.
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Feiler, Stefanie. "Parameter Estimation in Panels of Intercorrelated Time Series." [S.l. : s.n.], 2005. http://nbn-resolving.de/urn:nbn:de:bsz:16-opus-61708.
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Dai, Min. "Control of power converters for distributed generation applications." Connect to resource, 2005. http://rave.ohiolink.edu/etdc/view?acc%5Fnum=osu1124329850.
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Lertpiriyasuwat, Vatchara. "Real-time estimation of end-effector position and orientation for manufacturing robots /." Thesis, Connect to this title online; UW restricted, 2000. http://hdl.handle.net/1773/7047.
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Henter, Gustav Eje. "Probabilistic Sequence Models with Speech and Language Applications." Doctoral thesis, KTH, Kommunikationsteori, 2013. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-134693.
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Series data, sequences of measured values, are ubiquitous. Whenever observations are made along a path in space or time, a data sequence results. To comprehend nature and shape it to our will, or to make informed decisions based on what we know, we need methods to make sense of such data. Of particular interest are probabilistic descriptions, which enable us to represent uncertainty and random variation inherent to the world around us. This thesis presents and expands upon some tools for creating probabilistic models of sequences, with an eye towards applications involving speech and language. Modelling speech and language is not only of use for creating listening, reading, talking, and writing machines---for instance allowing human-friendly interfaces to future computational intelligences and smart devices of today---but probabilistic models may also ultimately tell us something about ourselves and the world we occupy. The central theme of the thesis is the creation of new or improved models more appropriate for our intended applications, by weakening limiting and questionable assumptions made by standard modelling techniques. One contribution of this thesis examines causal-state splitting reconstruction (CSSR), an algorithm for learning discrete-valued sequence models whose states are minimal sufficient statistics for prediction. Unlike many traditional techniques, CSSR does not require the number of process states to be specified a priori, but builds a pattern vocabulary from data alone, making it applicable for language acquisition and the identification of stochastic grammars. A paper in the thesis shows that CSSR handles noise and errors expected in natural data poorly, but that the learner can be extended in a simple manner to yield more robust and stable results also in the presence of corruptions. Even when the complexities of language are put aside, challenges remain. The seemingly simple task of accurately describing human speech signals, so that natural synthetic speech can be generated, has proved difficult, as humans are highly attuned to what speech should sound like. Two papers in the thesis therefore study nonparametric techniques suitable for improved acoustic modelling of speech for synthesis applications. Each of the two papers targets a known-incorrect assumption of established methods, based on the hypothesis that nonparametric techniques can better represent and recreate essential characteristics of natural speech. In the first paper of the pair, Gaussian process dynamical models (GPDMs), nonlinear, continuous state-space dynamical models based on Gaussian processes, are shown to better replicate voiced speech, without traditional dynamical features or assumptions that cepstral parameters follow linear autoregressive processes. Additional dimensions of the state-space are able to represent other salient signal aspects such as prosodic variation. The second paper, meanwhile, introduces KDE-HMMs, asymptotically-consistent Markov models for continuous-valued data based on kernel density estimation, that additionally have been extended with a fixed-cardinality discrete hidden state. This construction is shown to provide improved probabilistic descriptions of nonlinear time series, compared to reference models from different paradigms. The hidden state can be used to control process output, making KDE-HMMs compelling as a probabilistic alternative to hybrid speech-synthesis approaches. A final paper of the thesis discusses how models can be improved even when one is restricted to a fundamentally imperfect model class. Minimum entropy rate simplification (MERS), an information-theoretic scheme for postprocessing models for generative applications involving both speech and text, is introduced. MERS reduces the entropy rate of a model while remaining as close as possible to the starting model. This is shown to produce simplified models that concentrate on the most common and characteristic behaviours, and provides a continuum of simplifications between the original model and zero-entropy, completely predictable output. As the tails of fitted distributions may be inflated by noise or empirical variability that a model has failed to capture, MERS's ability to concentrate on high-probability output is also demonstrated to be useful for denoising models trained on disturbed data.QC 20131128
ACORNS: Acquisition of Communication and Recognition Skills
LISTA – The Listening Talker
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Kang, Youn-Soo. "Delay, Stop and Queue Estimation for Uniform and Random Traffic Arrivals at Fixed-Time Signalized Intersections." Diss., Virginia Tech, 2000. http://hdl.handle.net/10919/27030.
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With the introduction of different forms of adaptive and actuated signal control, there is a need for effective evaluation tools that can capture the intricacies of real-life applications. While the current state-of-the-art analytical procedures provide simple approaches for estimating delay, queue length and stops at signalized intersections, they are limited in scope. Alternatively, several microscopic simulation softwares are currently available for the evaluation of signalized intersections. The objective of this dissertation is fourfold. First, it evaluates the consistency, accuracy, limitations and scope of the alternative analytical models. Second, it evaluates the validity of micro simulation results that evolve as an outcome of the car-following relationships. The validity of these models is demonstrated for idealized hypothetical examples where analytical solutions can be derived. Third, the dissertation expands the scope of current analytical models for the evaluation of oversaturated signalized intersections. Finally, the dissertation demonstrates the implications of using analytical models for the evaluation of real-life network and traffic configurations.
This dissertation compared the delay estimates from numerous models for an undersaturated and oversaturated signalized intersection considering uniform and random arrivals in an attempt to systematically evaluate and demonstrate the assumptions and limitations of different delay estimation approaches. Specifically, the dissertation compared a theoretical vertical queuing analysis model, the queue-based models used in the 1994 and 2000 versions of the Highway Capacity Manual, the queue-based model in the 1995 Canadian Capacity Guide for Signalized Intersections, a theoretical horizontal queuing model derived from shock wave analysis, and the delay estimates produced by the INTEGRATION microscopic traffic simulation software. The results of the comparisons for uniform arrivals indicated that all delay models produced identical results under such traffic conditions, except for the estimates produced by the INTEGRATION software, which tended to estimate slightly higher delays than the other approaches. For the random arrivals, the results of the comparisons indicated that the delay estimates obtained by a micro-simulation model like INTEGRATION were consistent with the delay estimates computed by the analytical approaches.
In addition, this dissertation compared the number of stops and the maximum extent of queue estimates using analytical procedures and the INTEGRATION simulation model for both undersaturated and oversaturated signalized intersections to assess their consistency and to analyze their applicability. For the number of stops estimates, it is found that there is a general agreement between the INTEGRATION microscopic simulation model and the analytical models for undersaturated signalized intersections. Both uniform and random arrivals demonstrated consistency between the INTEGRATION model and the analytical procedures; however, at a v/c ratio of 1.0 the analytical models underestimate the number of stops. The research developed an upper limit and a proposed model for estimating the number of vehicle stops for oversaturated conditions. It was demonstrated that the current state-of-the-practice analytical models can provide stop estimates that far exceed the upper bound. On the other hand, the INTEGRATION model was found to be consistent with the upper bound and demonstrated that the number of stops converge to 2.3 as the v/c ratio tends to 2.0. For the maximum extent of queue estimates, the estimated maximum extent of queue predicted from horizontal shock wave analysis was higher than the predictions from vertical deterministic queuing analysis. The horizontal shock wave model predicted lower maximum extent of queue than the CCG 1995 model. For oversaturated conditions, the vertical deterministic queuing model underestimated the maximum queue length. It was found that the CCG 1995 predictions were lower than those from the horizontal shock wave model. These differences were attributed to the fact that the CCG 1995 model estimates the remaining residual queue at the end of evaluation time. A consistency was found between the INTEGRATION model and the horizontal shock wave model predictions with respect to the maximum extent of queue for both undersaturated and oversaturated signalized intersections.
Finally, the dissertation analyzed the impact of mixed traffic condition on the vehicle delay, person delay, and number of vehicle stops at a signalized intersection. The analysis considered approximating the mixed flow for equivalent homogeneous flows using two potential conversion factors. The first of these conversion factors was based on relative vehicle lengths while the second was based on relative vehicle riderships. The main conclusion of the analysis was that the optimum vehicle equivalency was dependent on the background level of congestion, the transit vehicle demand, and the Measure of Effectiveness (MOE) being considered. Consequently, explicit simulation of mixed flow is required in order to capture the unique vehicle interactions that result from mixed flow. Furthermore, while homogeneous flow approximations might be effective for some demand levels, these approximations are not consistently effective.Ph. D.
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Mercado-Ravell, Diego Alberto. "Autonomous navigation and teleoperation of unmanned aerial vehicles using monocular vision." Thesis, Compiègne, 2015. http://www.theses.fr/2015COMP2239/document.
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Ce travail porte, de façon théorétique et pratique, sur les sujets plus pertinents autour des drones en navigation autonome et semi-autonome. Conformément à la nature multidisciplinaire des problèmes étudies, une grande diversité des techniques et théories ont été couverts dans les domaines de la robotique, l’automatique, l’informatique, la vision par ordinateur et les systèmes embarques, parmi outres.Dans le cadre de cette thèse, deux plates-formes expérimentales ont été développées afin de valider la théorie proposée pour la navigation autonome d’un drone. Le premier prototype, développé au laboratoire, est un quadrirotor spécialement conçu pour les applications extérieures. La deuxième plate-forme est composée d’un quadrirotor à bas coût du type AR.Drone fabrique par Parrot. Le véhicule est connecté sans fil à une station au sol équipé d’un système d’exploitation pour robots (ROS) et dédié à tester, d’une façon facile, rapide et sécurisé, les algorithmes de vision et les stratégies de commande proposés. Les premiers travaux développés ont été basés sur la fusion de donnés pour estimer la position du drone en utilisant des capteurs inertiels et le GPS. Deux stratégies ont été étudiées et appliquées, le Filtre de Kalman Etendu (EKF) et le filtre à Particules (PF). Les deux approches prennent en compte les mesures bruitées de la position de l’UAV, de sa vitesse et de son orientation. On a réalisé une validation numérique pour tester la performance des algorithmes. Une tâche dans le cahier de cette thèse a été de concevoir d’algorithmes de commande pour le suivi de trajectoires ou bien pour la télé-opération. Pour ce faire, on a proposé une loi de commande basée sur l’approche de Mode Glissants à deuxième ordre. Cette technique de commande permet de suivre au quadrirotor de trajectoires désirées et de réaliser l’évitement des collisions frontales si nécessaire. Etant donné que la plate-forme A.R.Drone est équipée d’un auto-pilote d’attitude, nous avons utilisé les angles désirés de roulis et de tangage comme entrées de commande. L’algorithme de commande proposé donne de la robustesse au système en boucle fermée. De plus, une nouvelle technique de vision monoculaire par ordinateur a été utilisée pour la localisation d’un drone. Les informations visuelles sont fusionnées avec les mesures inertielles du drone pour avoir une bonne estimation de sa position. Cette technique utilise l’algorithme PTAM (localisation parallèle et mapping), qui s’agit d’obtenir un nuage de points caractéristiques dans l’image par rapport à une scène qui servira comme repère. Cet algorithme n’utilise pas de cibles, de marqueurs ou de scènes bien définies. La contribution dans cette méthodologie a été de pouvoir utiliser le nuage de points disperse pour détecter possibles obstacles en face du véhicule. Avec cette information nous avons proposé un algorithme de commande pour réaliser l’évitement d’obstacles. Cette loi de commande utilise les champs de potentiel pour calculer une force de répulsion qui sera appliquée au drone. Des expériences en temps réel ont montré la bonne performance du système proposé. Les résultats antérieurs ont motivé la conception et développement d’un drone capable de réaliser en sécurité l’interaction avec les hommes et les suivre de façon autonome. Un classificateur en cascade du type Haar a été utilisé pour détecter le visage d’une personne. Une fois le visage est détecté, on utilise un filtre de Kalman (KF) pour améliorer la détection et un algorithme pour estimer la position relative du visage. Pour réguler la position du drone et la maintenir à une distance désirée du visage, on a utilisé une loi de commande linéaireThe present document addresses, theoretically and experimentally, the most relevant topics for Unmanned Aerial Vehicles (UAVs) in autonomous and semi-autonomous navigation. According with the multidisciplinary nature of the studied problems, a wide range of techniques and theories are covered in the fields of robotics, automatic control, computer science, computer vision and embedded systems, among others. As part of this thesis, two different experimental platforms were developed in order to explore and evaluate various theories and techniques of interest for autonomous navigation. The first prototype is a quadrotor specially designed for outdoor applications and was fully developed in our lab. The second testbed is composed by a non expensive commercial quadrotor kind AR. Drone, wireless connected to a ground station equipped with the Robot Operating System (ROS), and specially intended to test computer vision algorithms and automatic control strategies in an easy, fast and safe way. In addition, this work provides a study of data fusion techniques looking to enhance the UAVs pose estimation provided by commonly used sensors. Two strategies are evaluated in particular, an Extended Kalman Filter (EKF) and a Particle Filter (PF). Both estimators are adapted for the system under consideration, taking into account noisy measurements of the UAV position, velocity and orientation. Simulations show the performance of the developed algorithms while adding noise from real GPS (Global Positioning System) measurements. Safe and accurate navigation for either autonomous trajectory tracking or haptic teleoperation of quadrotors is presented as well. A second order Sliding Mode (2-SM) control algorithm is used to track trajectories while avoiding frontal collisions in autonomous flight. The time-scale separation of the translational and rotational dynamics allows us to design position controllers by giving desired references in the roll and pitch angles, which is suitable for quadrotors equipped with an internal attitude controller. The 2-SM control allows adding robustness to the closed-loop system. A Lyapunov based analysis probes the system stability. Vision algorithms are employed to estimate the pose of the vehicle using only a monocular SLAM (Simultaneous Localization and Mapping) fused with inertial measurements. Distance to potential obstacles is detected and computed using the sparse depth map from the vision algorithm. For teleoperation tests, a haptic device is employed to feedback information to the pilot about possible collisions, by exerting opposite forces. The proposed strategies are successfully tested in real-time experiments, using a low-cost commercial quadrotor. Also, conception and development of a Micro Aerial Vehicle (MAV) able to safely interact with human users by following them autonomously, is achieved in the present work. Once a face is detected by means of a Haar cascade classifier, it is tracked applying a Kalman Filter (KF), and an estimation of the relative position with respect to the face is obtained at a high rate. A linear Proportional Derivative (PD) controller regulates the UAV’s position in order to keep a constant distance to the face, employing as well the extra available information from the embedded UAV’s sensors. Several experiments were carried out through different conditions, showing good performance even under disadvantageous scenarios like outdoor flight, being robust against illumination changes, wind perturbations, image noise and the presence of several faces on the same image. Finally, this thesis deals with the problem of implementing a safe and fast transportation system using an UAV kind quadrotor with a cable suspended load. The objective consists in transporting the load from one place to another, in a fast way and with minimum swing in the cable
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Schoenig, Gregory Neumann. "Contributions to Robust Adaptive Signal Processing with Application to Space-Time Adaptive Radar." Diss., Virginia Tech, 2007. http://hdl.handle.net/10919/26972.
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Classical adaptive signal processors typically utilize assumptions in their derivation. The
presence of adequate Gaussian and independent and identically distributed (i.i.d.) input
data are central among such assumptions. However, classical processors have a tendency
to suffer a degradation in performance when assumptions like these are violated. Worse
yet, such degradation is not guaranteed to be proportional to the level of deviation from
the assumptions. This dissertation proposes new signal processing algorithms based on
aspects of modern robustness theory, including methods to enable adaptivity of presently
non-adaptive robust approaches. The contributions presented are the result of research
performed jointly in two disciplines, namely robustness theory and adaptive signal process-
ing. This joint consideration of robustness and adaptivity enables improved performance in
assumption-violating scenarios â scenarios in which classical adaptive signal processors fail.
Three contributions are central to this dissertation. First, a new adaptive diagnostic tool for
high-dimension data is developed and shown robust in problematic contamination. Second,
a robust data-pre-whitening method is presented based on the new diagnostic tool. Finally,
a new suppression-based robust estimator is developed for use with complex-valued adaptive
signal processing data. To exercise the proposals and compare their performance to state-
of-the art methods, data sets commonly used in statistics as well as Space-Time Adaptive
Processing (STAP) radar data, both real and simulated, are processed, and performance is
subsequently computed and displayed. The new algorithms are shown to outperform their
state-of-the-art counterparts from both a signal-to-interference plus noise ratio (SINR) conver-
gence rate and target detection perspective.Ph. D.
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Nogueira, Samuel Lourenço. "Sistemas Markovianos para estimativa de ângulos absolutos em exoesqueletos de membros inferiores." Universidade de São Paulo, 2015. http://www.teses.usp.br/teses/disponiveis/18/18149/tde-19052015-172242/.
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Nesta tese de doutorado são apresentados sistemas globais de estimativa baseados em modelos Markovianos aplicados na área de reabilitação robótica. Os sistemas propostos foram desenvolvidos para estimar as posições angulares dos elos de exoesqueletos para membros inferiores, desenvolvidos para reabilitação motora em pacientes que sofreram Acidente Vascular Cerebral (AVC) ou lesão medular. Filtros baseados no filtro de Kalman, um nominal e outro considerando incertezas no modelo, foram utilizados em estratégias de fusão de dados de sensores provenientes de sensores inerciais, possibilitando estimativas de posicionamentos angulares. Algoritmos genéticos são utilizados na otimização dos filtros, ajustando as matrizes de peso destes. Em oposição as modelagens tradicionais, via estimativa local, utilizando somente uma unidade inercial para cada modelo, propõe-se um sistema global de estimativa, obtendo-se a melhor informação de cada sensor combinando-os em um modelo Markoviano. Resultados experimentais com um exoesqueleto foram utilizados para comparar a abordagem Markoviana às convencionais.In this thesis are presented global estimation systems based on Markov models applied in robotic rehabilitation area. The proposed systems have been developed to estimate the angular positions of the exoskeletons for lower limbs, designed to provide motor rehabilitation of stroke and spinal cord injured people. Filters based on the Kalman filter, one nominal and other considering uncertainties in the model, were used in sensor data fusion strategies from inertial sensors, to estimate angular positions. Genetic algorithms are used to the optimization of filters, tuning the weighting matrices. In opposition to these modelling via local estimation, using only one inertial unit, we also chose a global modelling getting the best information from each sensor, combining them in a Markov model. Experimental results with an exoskeleton were used to compare the Markovian approach to conventional.
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Rahmani, Mahmood. "Urban Travel Time Estimation from Sparse GPS Data : An Efficient and Scalable Approach." Doctoral thesis, KTH, Transportplanering, ekonomi och teknik, 2015. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-167798.
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The use of GPS probes in traffic management is growing rapidly as the required data collection infrastructure is increasingly in place, with significant number of mobile sensors moving around covering expansive areas of the road network. Many travelers carry with them at least one device with a built-in GPS receiver. Furthermore, vehicles are becoming more and more location aware. Vehicles in commercial fleets are now routinely equipped with GPS. Travel time is important information for various actors of a transport system, ranging from city planning, to day to day traffic management, to individual travelers. They all make decisions based on average travel time or variability of travel time among other factors. AVI (Automatic Vehicle Identification) systems have been commonly used for collecting point-to-point travel time data. Floating car data (FCD) -timestamped locations of moving vehicles- have shown potential for travel time estimation. Some advantages of FCD compared to stationary AVI systems are that they have no single point of failure and they have better network coverage. Furthermore, the availability of opportunistic sensors, such as GPS, makes the data collection infrastructure relatively convenient to deploy. Currently, systems that collect FCD are designed to transmit data in a limited form and relatively infrequently due to the cost of data transmission. Thus, reported locations are far apart in time and space, for example with 2 minutes gaps. For sparse FCD to be useful for transport applications, it is required that the corresponding probes be matched to the underlying digital road network. Matching such data to the network is challenging. This thesis makes the following contributions: (i) a map-matching and path inference algorithm, (ii) a method for route travel time estimation, (iii) a fixed point approach for joint path inference and travel time estimation, and (iv) a method for fusion of FCD with data from automatic number plate recognition. In all methods, scalability and overall computational efficiency are considered among design requirements. Throughout the thesis, the methods are used to process FCD from 1500 taxis in Stockholm City. Prior to this work, the data had been ignored because of its low frequency and minimal information. The proposed methods proved that the data can be processed and transformed into useful traffic information. Finally, the thesis implements the main components of an experimental ITS laboratory, called iMobility Lab. It is designed to explore GPS and other emerging data sources for traffic monitoring and control. Processes are developed to be computationally efficient, scalable, and to support real time applications with large data sets through a proposed distributed implementation.QC 20150525
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HUSSAIN, MOAZZAM. "A Real-time Absolute Position Estimation Architecture for Autonomous Aerial Robots using Artificial Neural Networks." Doctoral thesis, Politecnico di Torino, 2014. http://hdl.handle.net/11583/2542487.
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The civil applications of Unmanned Aerial Vehicle (UAV) technology are constantly on a rise and the safety rules for the operation of UAVs in populated areas are being drafted. The UAV technology is an active area of academic research due to the challenges related to aerodynamics, tight power and payload budgets, multi-sensor information fusion, reactive real-time path planning, perception and communication bandwidth requirements. Autonomous navigation is a complex problem due to the challenges of algorithmic complexity and their real-time implementation. The challenges like long-term GPS errors/outage/jamming and exponential error growth in inertial sensors increase the complexity of autonomous navigation to an extent that high level of redundancy is mandatory in the design of navigation systems. Typical UAV systems use multi-sensor (GPS + INS +Vision) data fusion coupled with responsive sensors, innovative navigation algorithms, computationally capable onboard computers and reactive electromechanical systems to accomplish the navigational needs of safe operations in urban environments.
Machine learning is a very promising technology and has broad applicability in the many real-life problems: ranging from hand-held & wearable computers to intelligent cars and homes. It can be efficiently used in autonomous navigation of UAVs. This work presents a novel absolute position estimation solution that leverages Radial Basis Function (RBF) classifier for robust aerial image registration. The proposed solution covers the entire spectrum of the problem involving algorithm design, hardware architecture and real-time hardware implementation. The system relies on single passive imaging source for acquisition of aerial images. The sensed image is geometrically transformed to bring it in a common view point as the reference satellite image. The orthorectified aerial image is then learned by the RBF network and full search is performed in the Region of Interest (ROI) of the reference satellite image. The real-time implementation of computationally intensive algorithm is accomplished by designing a customized wide data path in Field Programmable Gate Array (FPGA). The proposed architecture offers a reliable drift-free position estimation solution by conglomerating information from the inertial sensors and geo-registration of the aerial images over a geodetically aligned satellite reference image. We compare the robustness of our proposed matching algorithm with the standard normalized area correlation techniques and present limitations and False Acceptance Rates (FAR) of the two algorithms. This analysis has been performed on a set of real aerial and satellite imagery, acquired under different lightening and weather conditions. This is then followed by a discussion on real-time FPGA based architecture and power analysis. We conclude by presenting future directions of the work.
Keywords: Inertial Measurement Units, Vision based Navigation, Real-time implementation, FPGA, Neural Network
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Völker, Marten [Verfasser]. "Linear Robust Control of a Nonlinear and Time-varying Process : A Two-step Approach to the Multi-objective Synthesis of Fixed-order Controllers / Marten Völker." Aachen : Shaker, 2007. http://d-nb.info/1164339648/34.
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Carraro, Marco. "Real-time RGB-Depth preception of humans for robots and camera networks." Doctoral thesis, Università degli studi di Padova, 2018. http://hdl.handle.net/11577/3426800.
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This thesis deals with robot and camera network perception using RGB-Depth data. The goal is to provide efficient and robust algorithms for interacting with humans. For this reason, a special care has been devoted to design algorithms which can run in real-time on consumer computers and embedded cards.
The main contribution of this thesis is the 3D body pose estimation of the human body. We propose two novel algorithms which take advantage of the data stream of a RGB-D camera network outperforming the state-of-the-art performance in both single-view and multi-view tests. While the first algorithm works on point cloud data which is feasible also with no external light, the second one performs better, since it deals with multiple persons with negligible overhead and does not rely on the synchronization between the different cameras in the network.
The second contribution regards long-term people re-identification in camera networks. This is particularly challenging since we cannot rely on appearance cues, in order to be able to re-identify people also in different days. We address this problem by proposing a face-recognition framework based on a Convolutional Neural Network and a Bayes inference system to re-assign the correct ID and person name to each new track.
The third contribution is about Ambient Assisted Living. We propose a prototype of an assistive robot which periodically patrols a known environment, reporting unusual events as people fallen on the ground. To this end, we developed a fast and robust approach which can work also in dimmer scenes and is validated using a new publicly-available RGB-D dataset recorded on-board of our open-source robot prototype.
As a further contribution of this work, in order to boost the research on this topics and to provide the best benefit to the robotics and computer vision community, we released under open-source licenses most of the software implementations of the novel algorithms described in this work.Questa tesi tratta di percezione per robot autonomi e per reti di telecamere da dati RGB-Depth. L'obiettivo è quello di fornire algoritmi robusti ed efficienti per l'interazione con le persone. Per questa ragione, una particolare attenzione è stata dedicata allo sviluppo di soluzioni efficienti che possano essere eseguite in tempo reale su computer e schede grafiche consumer. Il contributo principale di questo lavoro riguarda la stima automatica della posa 3D del corpo delle persone presenti in una scena. Vengono proposti due algoritmi che sfruttano lo stream di dati RGB-Depth da una rete di telecamere andando a migliorare lo stato dell'arte sia considerando dati da singola telecamera che usando tutte le telecamere disponibili. Il secondo algoritmo ottiene risultati migliori in quanto riesce a stimare la posa di tutte le persone nella scena con overhead trascurabile e non richiede sincronizzazione tra i vari nodi della rete. Tuttavia, il primo metodo utilizza solamente nuvole di punti che sono disponibili anche in ambiente con poca luce nei quali il secondo algoritmo non raggiungerebbe gli stessi risultati. Il secondo contributo riguarda la re-identificazione di persone a lungo termine in reti di telecamere. Questo problema è particolarmente difficile in quanto non si può contare su feature di colore o che considerino i vestiti di ogni persona, in quanto si vuole che il riconoscimento funzioni anche a distanza di giorni. Viene proposto un framework che sfrutta il riconoscimento facciale utilizzando una Convolutional Neural Network e un sistema di classificazione Bayesiano. In questo modo, ogni qual volta viene generata una nuova traccia dal sistema di people tracking, la faccia della persona viene analizzata e, in caso di match, il vecchio ID viene riassegnato. Il terzo contributo riguarda l'Ambient Assisted Living. Abbiamo proposto e implementato un robot di assistenza che ha il compito di sorvegliare periodicamente un ambiente conosciuto, riportando eventi non usuali come la presenza di persone a terra. A questo fine, abbiamo sviluppato un approccio veloce e robusto che funziona anche in assenza di luce ed è stato validato usando un nuovo dataset RGB-Depth registrato a bordo robot. Con l'obiettivo di avanzare la ricerca in questi campi e per fornire il maggior beneficio possibile alle community di robotica e computer vision, come contributo aggiuntivo di questo lavoro, abbiamo rilasciato, con licenze open-source, la maggior parte delle implementazioni software degli algoritmi descritti in questo lavoro.
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Mody, Apurva Narendra. "Signal Acquisition and Tracking for Fixed Wireless Access Multiple Input Multiple Output Orthogonal Frequency Division Multiplexing." Diss., Georgia Institute of Technology, 2004. http://hdl.handle.net/1853/7624.
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The general objective of this proposed research is to design and develop signal acquisition and tracking algorithms for multiple input multiple output orthogonal frequency division multiplexing (MIMO-OFDM) systems for fixed wireless access applications. The algorithms are specifically targeted for systems that work in time division multiple access and frequency division multiple access frame modes. In our research, we first develop a comprehensive system model for a MIMO-OFDM system under the influence of the radio frequency (RF) oscillator frequency offset, sampling frequency (SF) offset, RF oscillator phase noise, frequency selective channel impairments and finally the additive white Gaussian noise. We then develop the acquisition and tracking algorithms to estimate and track all these parameters. The acquisition and tracking algorithms are assisted by a preamble consisting of one or more training sequences and pilot symbol matrices. Along with the signal acquisition and tracking algorithms, we also consider design of the MIMO-OFDM preamble and pilot signals that enable the suggested algorithms to work efficiently.
Signal acquisition as defined in our research consists of time and RF synchronization, SF offset estimation and correction, phase noise estimation and correction and finally channel estimation. Signal tracking consists of RF, SF, phase noise and channel tracking. Time synchronization, RF oscillator frequency offset, SF oscillator frequency offset, phase noise and channel estimation and tracking are all research topics by themselves. A large number of studies have addressed these issues, but usually individually and for single-input single-output (SISO) OFDM systems. In the proposed research we present a complete suite of signal acquisition and tracking algorithms for MIMO-OFDM systems along with Cramr-Rao bounds for the SISO-OFDM case. In addition, we also derive the Maximum Likelihood (ML) estimates of the parameters for the SISO-OFDM case.
Our proposed research is unique from the existing literature in that it presents a complete receiver implementation for MIMO-OFDM systems and accounts for the cumulative effects of all possible acquisition and tracking errors on the bit error rate (BER) performance. The suggested algorithms and the pilot/training schemes may be applied to any MIMO OFDM system and are independent of the space-time coding techniques that are employed.
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Atchuthan, Dinesh. "Towards new sensing capabilities for legged locomotion using real-time state estimation with low-cost IMUs." Thesis, Toulouse 3, 2018. http://www.theses.fr/2018TOU30316/document.
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L'estimation en robotique est un sujet important affecté par les compromis entre certains critères majeurs parmi lesquels nous pouvons citer le temps de calcul et la précision. L'importance de ces deux critères dépend de l'application. Si le temps de calcul n'est pas important pour les méthodes hors ligne, il devient critique lorsque l'application doit s'exécuter en temps réel. De même, les exigences de précision dépendent des applications. Les estimateurs EKF sont largement utilisés pour satisfaire les contraintes en temps réel tout en obtenant une estimation avec des précisions acceptables. Les centrales inertielles (Inertial Measurement Unit - IMU) demeurent des capteurs répandus dnas les problèmes d'estimation de trajectoire. Ces capteurs ont par ailleurs la particularité de fournir des données à une fréquence élevée. La principale contribution de cette thèses est une présentation claire de la méthode de préintégration donnant lieu à une meilleure utilisation des centrales inertielles. Nous appliquons cette méthode aux problèmes d'estimation dans les cas de la navigation piétonne et celle des robots humanoïdes. Nous souhaitons par ailleurs montrer que l'estimation en temps réel à l'aide d'une centrale inertielle à faible coût est possible avec des méthodes d'optimisation tout en formulant les problèmes à l'aide d'un modèle graphique bien que ces méthodes soient réputées pour leurs coûts élevés en terme de calculs. Nous étudions également la calibration des centrales inertielles, une étape qui demeure critique pour leurs utilisations. Les travaux réalisés au cours de cette thèse ont été pensés en gardant comme perspective à moyen terme le SLAM visuel-inertiel. De plus, ce travail aborde une autre question concernant les robots à jambes. Contrairement à leur architecture habituelle, pourrions-nous utiliser plusieurs centrales inertielles à faible coût sur le robot pour obtenir des informations précieuses sur le mouvement en cours d'exécution ?Estimation in robotics is an important subject affected by trade-offs between some major critera from which we can cite the computation time and the accuracy. The importance of these two criteria are application-dependent. If the computation time is not important for off-line methods, it becomes critical when the application has to run on real-time. Similarly, accuracy requirements are dependant on the applications. EKF estimators are widely used to satisfy real-time constraints while achieving acceptable accuracies. One sensor widely used in trajectory estimation problems remains the inertial measurement units (IMUs) providing data at a high rate. The main contribution of this thesis is a clear presentation of the preintegration theory yielding in a better use IMUs. We apply this method for estimation problems in both pedestrian and humanoid robots navigation to show that real-time estimation using a low- cost IMU is possible with smoothing methods while formulating the problems with a factor graph. We also investigate the calibration of the IMUs as it is a critical part of those sensors. All the development made during this thesis was thought with a visual-inertial SLAM background as a mid-term perspective. Firthermore, this work tries to rise another question when it comes to legged robots. In opposition to their usual architecture, could we use multiple low- cost IMUs on the robot to get valuable information about the motion being executed?
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Fan, Ming. "Real-Time Scheduling of Embedded Applications on Multi-Core Platforms." FIU Digital Commons, 2014. http://digitalcommons.fiu.edu/etd/1243.
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For the past several decades, we have experienced the tremendous growth, in both scale and scope, of real-time embedded systems, thanks largely to the advances in IC technology. However, the traditional approach to get performance boost by increasing CPU frequency has been a way of past. Researchers from both industry and academia are turning their focus to multi-core architectures for continuous improvement of computing performance. In our research, we seek to develop efficient scheduling algorithms and analysis methods in the design of real-time embedded systems on multi-core platforms. Real-time systems are the ones with the response time as critical as the logical correctness of computational results. In addition, a variety of stringent constraints such as power/energy consumption, peak temperature and reliability are also imposed to these systems. Therefore, real-time scheduling plays a critical role in design of such computing systems at the system level.
We started our research by addressing timing constraints for real-time applications on multi-core platforms, and developed both partitioned and semi-partitioned scheduling algorithms to schedule fixed priority, periodic, and hard real-time tasks on multi-core platforms. Then we extended our research by taking temperature constraints into consideration. We developed a closed-form solution to capture temperature dynamics for a given periodic voltage schedule on multi-core platforms, and also developed three methods to check the feasibility of a periodic real-time schedule under peak temperature constraint. We further extended our research by incorporating the power/energy constraint with thermal awareness into our research problem. We investigated the energy estimation problem on multi-core platforms, and developed a computation efficient method to calculate the energy consumption for a given voltage schedule on a multi-core platform. In this dissertation, we present our research in details and demonstrate the effectiveness and efficiency of our approaches with extensive experimental results.
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Svenzén, Niklas. "Real Time Implementation of Map Aided Positioning Using a Bayesian Approach." Thesis, Linköping University, Department of Electrical Engineering, 2002. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-1493.
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With the simple means of a digitized map and the wheel speed signals, it is possible to position a vehicle with an accuracy comparable to GPS. The positioning problem is a non-linear filtering problem and a particle filter has been applied to solve it. Two new approaches studied are the Auxiliary Particle Filter (APF), that aims at lowerering the variance of the error, and Rao-Blackwellization that exploits the linearities in the model. The results show that these methods require problems of higher complexity to fully utilize their advantages.
Another aspect in this thesis has been to handle off-road driving scenarios, using dead reckoning. An off road detection mechanism has been developed and the results show that off-road driving can be detected accurately. The algorithm has been successfully implemented on a hand-held computer by quantizing the particle filter while keeping good filter performance.
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Stöter, Fabian-Robert [Verfasser], Bernd [Akademischer Betreuer] Edler, Bernd [Gutachter] Edler, and Gael [Gutachter] Richard. "Separation and Count Estimation for Audio Sources Overlapping in Time and Frequency / Fabian-Robert Stöter ; Gutachter: Bernd Edler, Gael Richard ; Betreuer: Bernd Edler." Erlangen : Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), 2020. http://d-nb.info/1203879490/34.
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Do, Manh Hung. "Synthèse robuste d'observateurs pour systèmes singuliers linéaires à paramètres variants." Thesis, Université Grenoble Alpes, 2020. http://www.theses.fr/2020GRALT053.
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Cette thèse s'inscrit dans le cadre de l'étude de l'estimation d'état et des défauts des systèmes dynamiques Linéaires à Paramètres Variants (LPV). La thèse s'attache à considérer deux classes de systèmes : les systèmes réguliers et les systèmes singuliers. Les estimateurs proposés sont synthétisés pour être robustes aux incertitudes paramétriques, aux perturbations présentes sur l'équation d'état et de sortie, aux bruits de mesures, aux non-linéarités Lipchitziennes et aux retards. Les contributions majeures de ses travaux sont respectivement : la conception simultanée d'un régulateur et d'un observateur pour un système LPV incertain avec l'atténuation des perturbations par modelage fréquentielle des sorties, la conception d'observateurs pour l'estimation des défaillances/dégradations avec découplage partiel des entrées inconnues, la synthèse H∞ et H2 d'observateurs réguliers pour les systèmes singuliers avec entrée Lipchitzienne, et la synthèse H∞ d'un observateur régulier pour un système LPV à retards. La qualité des estimations est validée avec des données de terrain (plateforme INOVE) et des exemples numériquesThis Thesis is focused on the study of state and fault estimation in Linear Parameter-Varying (LPV) systems. The Thesis considers two classes of systems: non-singular and singular systems. In specific, the proposed observers are synthesized to be robust against parametric uncertainties, input and output disturbances, measurement noise, Lipschitz nonlinearities, and time delays. The major contributions of this research are respectively: an integrated observer-controller design for uncertain LPV systems with a new methodology of disturbance attenuation called output frequency-shaping filter; the design and the development of unknown input (UI) observers for fault estimation under the existence of partially decoupled UIs; the synthesis of H∞ and H2 observers for the singular system with Lipschitz nonlinearity; and a H∞ observer design for time-delay LPV system. Finally, the performance of the proposed methods is justified by laboratory experiments with INOVE platform and numerical examples