Bibliographies thématiques / Cardinalized probability hypothesis density

Littérature scientifique sur le sujet « Cardinalized probability hypothesis density »

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Publié le 10 mars 2023

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Articles de revues sur le sujet "Cardinalized probability hypothesis density"

Li, Bo, et Fu-Wen Pang. « Improved cardinalized probability hypothesis density filtering algorithm ». Applied Soft Computing 24 (novembre 2014) : 692–703. http://dx.doi.org/10.1016/j.asoc.2014.08.023.

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Vo, Ba-Tuong, Ba-Ngu Vo et Antonio Cantoni. « Analytic Implementations of the Cardinalized Probability Hypothesis Density Filter ». IEEE Transactions on Signal Processing 55, n^o 7 (juillet 2007) : 3553–67. http://dx.doi.org/10.1109/tsp.2007.894241.

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Ma, Yue, Jian-zhang Zhu, Qian-qing Qin et Yi-jun Hu. « Convolution kernels implementation of cardinalized probability hypothesis density filter ». Acta Mathematicae Applicatae Sinica, English Series 29, n^o 4 (octobre 2013) : 739–48. http://dx.doi.org/10.1007/s10255-013-0252-0.

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LIN, Zai-Ping, Yi-Yu ZHOU et Wei AN. « Track-Before-Detect algorithm based on cardinalized probability hypothesis density filter ». Journal of Infrared and Millimeter Waves 32, n^o 5 (2013) : 437. http://dx.doi.org/10.3724/sp.j.1010.2013.00437.

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Song, L., M. Liang et H. Ji. « Box-Particle Implementation and Comparison of Cardinalized Probability Hypothesis Density Filter ». Radioengineering 25, n^o 1 (14 avril 2016) : 177–86. http://dx.doi.org/10.13164/re.2016.0177.

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Ulmke, Martin, Ozgur Erdinc et Peter Willett. « GMTI Tracking via the Gaussian Mixture Cardinalized Probability Hypothesis Density Filter ». IEEE Transactions on Aerospace and Electronic Systems 46, n^o 4 (octobre 2010) : 1821–33. http://dx.doi.org/10.1109/taes.2010.5595597.

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Li, Bo, Huawei Yi et Xiaohui Li. « Innovative unscented transform–based particle cardinalized probability hypothesis density filter for multi-target tracking ». Measurement and Control 52, n^o 9-10 (21 octobre 2019) : 1567–78. http://dx.doi.org/10.1177/0020294019877494.

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Multi-target tracking is widely applied in video surveillance systems. As we know, although the standard particle cardinalized probability hypothesis density filter can estimate state of targets, it is difficult to define the proposal distribution function in prediction stage. Since the robust particles cannot be effectively drawn, the actual tracking accuracy should be enhanced. In this paper, an innovative unscented transform–based particle cardinalized probability hypothesis density filter is derived. Considering the different state spaces, we use the auxiliary particle method and then draw robust particles from the modified distributions in order to estimate the position of targets. Simultaneously, we present the recursion of the optimized Kalman gain to improve the general unscented transform for the velocity estimates. Using the track label, we further integrate them in the framework of the jump Markov model. The simulation results show that the proposed filter has advances in the multi-target tracking scenes. Moreover, the experiments indicate that the filter can track mobile targets with satisfactory results.

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Zhai Dai-Liang, Lei Hu-Min, Li Hai-Ning, Zhang Xu et Li Jiong. « Derivation of cardinalized probability hypothesis density filter via the physical-space approach ». Acta Physica Sinica 63, n^o 22 (2014) : 220204. http://dx.doi.org/10.7498/aps.63.220204.

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Lian, Feng, Chongzhao Han, Weifeng Liu, Jing Liu et Jian Sun. « Unified cardinalized probability hypothesis density filters for extended targets and unresolved targets ». Signal Processing 92, n^o 7 (juillet 2012) : 1729–44. http://dx.doi.org/10.1016/j.sigpro.2012.01.009.

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Franken, D., M. Schmidt et M. Ulmke. « "Spooky Action at a Distance" in the Cardinalized Probability Hypothesis Density Filter ». IEEE Transactions on Aerospace and Electronic Systems 45, n^o 4 (octobre 2009) : 1657–64. http://dx.doi.org/10.1109/taes.2009.5310327.

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Thèses sur le sujet "Cardinalized probability hypothesis density"

Jerrelind, Jakob. « Tracking of Pedestrians Using Multi-Target Tracking Methods with a Group Representation ». Thesis, Linköpings universitet, Reglerteknik, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-172579.

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Multi-target tracking (MTT) methods estimate the trajectory of targets from noisy measurement; therefore, they can be used to handle the pedestrian-vehicle interaction for a moving vehicle. MTT has an important part in assisting the Automated Driving System and the Advanced Driving Assistance System to avoid pedestrian-vehicle collisions. ADAS and ADS rely on correct estimates of the pedestrians' position and velocity, to avoid collisions or unnecessary emergency breaking of the vehicle. Therefore, to help the risk evaluation in these systems, the MTT needs to provide accurate and robust information of the trajectories (in terms of position and velocity) of the pedestrians in different environments. Several factors can make this problem difficult to handle for instance in crowded environments the pedestrians can suffer from occlusion or missed detection. Classical MTT methods, such as the global nearest neighbour filter, can in crowded environments fail to provide robust and accurate estimates. Therefore, more sophisticated MTT methods should be used to increase the accuracy and robustness and, in general, to improve the tracking of targets close to each other. The aim of this master's thesis is to improve the situational awareness with respect to pedestrians and pedestrian-vehicle interactions. In particular, the task is to investigate if the GM-PHD and the GM-CPHD filter improve pedestrian tracking in urban environments, compared to other methods presented in the literature. The proposed task can be divided into three parts that deal with different issues. The first part regards the significance of different clustering methods and how the pedestrians are grouped together. The implemented algorithms are the distance partitioning algorithm and the Gaussian mean shift clustering algorithm. The second part regards how modifications of the measurement noise levels and the survival of targets based on the target location, with respect to the vehicle's position, can improve the tracking performance and remove unwanted estimates. Finally, the last part regards the impact the filter estimates have on the tracking performance and how important accurate detections of the pedestrians are to improve the overall tracking. From the result the distance partitioning algorithm is the favourable algorithm, since it does not split larger groups. It is also seen that the proposed filters provide correct estimates of pedestrians in events of occlusion or missed detections but suffer from false estimates close to the ego vehicle due to uncertain detections. For the comparison, regarding the improvements, a classic standard MTT filter applying the global nearest neighbour method for the data association is used as the baseline. To conclude; the GM-CPHD filter proved to be the best out of the two proposed filters in this thesis work and performed better also compared to other methods known in the literature. In particular, its estimates survived for a longer period of time in presence of missed detection or occlusion. The conclusion of this thesis work is that the GM-CPHD filter improves the tracking performance and the situational awareness of the pedestrians.

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Clark, Daniel Edward. « Multiple target tracking with the probability hypothesis density filter ». Thesis, Heriot-Watt University, 2006. http://hdl.handle.net/10399/161.

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Li, Tiancheng. « Efficient particle implementation of Bayesian and probability hypothesis density filtering ». Thesis, London South Bank University, 2013. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.631738.

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Using a set of samples that are associated with weights (namely the particle method) to represent the distribution of interest for filtering under very general hypotheses (often referred to as the sequential Monte Carlo, SMC approaches or particle filters) has gained high attention in the last two decades. However, the particle method suffers from problems such as sample depletion, huge computational time and challenges raised in multi-target tracking (MTT). Aiming to address these problems and challenges, this thesis investigates efficient particle filtering from two perspectives that deal with different number of objects of interest: single-object and multi-object. On one side, novel resampling schemes, fast implementations of particle filters are developed under the Bayesian framework. On the other side, improved particle implementations of the probability hypothesis density (PHD) filter, namely the particle PHD filters are presented to deal with MTT. Resampling is a critical step in the implementation of particle filters that is of practical and theoretical significance. Firstly, various resampling methods and new developments are compared and classified into different categories, providing a comprehensive overview of resampling methods. General discussions about statistical effects of resampling are given with emphasis on robustness and identical distribution testing. New deterministic, adaptive and fast resampling schemes are put forward separately. Further, to increase the computing speed of the particle filter, a fast likelihood computing method based on numerical fitting is proposed, where the likelihood of particles is numerically fitted by the likelihood probability density function (Li-PDF) instead of directly computing it based on measurements. This is the first attempt that applies numerical fitting to enable real-time particle filtering.

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Lee, Chee Sing. « Simultaneous localization and mapping using single cluster probability hypothesis density filters ». Doctoral thesis, Universitat de Girona, 2015. http://hdl.handle.net/10803/323637.

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The majority of research in feature-based SLAM builds on the legacy of foundational work using the EKF, a single-object estimation technique. Because feature-based SLAM is an inherently multi-object problem, this has led to a number of suboptimalities in popular solutions. We develop an algorithm using the SC-PHD filter, a multi-object estimator modeled on cluster processes. This algorithm hosts capabilities not typically seen with feature-base SLAM solutions such as principled handling of clutter measurements and missed detections, and navigation with a mixture of stationary and moving landmarks. We present experiments with the SC-PHD SLAM algorithm on both synthetic and real datasets using an autonomous underwater vehicle. We compare our method to the RB-PHD SLAM, showing that it requires fewer approximations in its derivation and thus achieves superior performance.
En aquesta tesis es desenvolupa aquest algoritme a partir d’un filtre PHD amb un únic grup (SC-PHD), una tècnica d’estimació multi-objecte basat en processos d’agrupació. Aquest algoritme té unes capacitats que normalment no es veuen en els algoritmes de SLAM basats en característiques, ja que és capaç de tractar falses característiques, així com característiques no detectades pels sensors del vehicle, a més de navegar en un entorn amb la presència de característiques estàtiques i característiques en moviment de forma simultània. Es presenten els resultats experimentals de l’algoritme SC-PHD en entorns reals i simulats utilitzant un vehicle autònom submarí. Els resultats són comparats amb l’algoritme de SLAM Rao-Blackwellized PHD (RB-PHD), demostrant que es requereixen menys aproximacions en la seva derivació i en conseqüència s’obté un rendiment superior.

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Swain, Anthony Jack. « Group and extended target tracking with the Probability Hypothesis Density filter ». Thesis, Heriot-Watt University, 2013. http://hdl.handle.net/10399/2839.

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Multiple target tracking concerns the estimation of an unknown and time-varying number of objects (targets) as they dynamically evolve over time from a sequence of measurements obtained from sensors at discrete time intervals. In the Bayesian filtering framework the estimation problem incorporates natural phenomena such as false measurements and target birth/death. Though theoretically optimal, the generally intractable Bayesian filter requires suitable approximations. This thesis is particularly motivated by a first-order moment approximation known as the Probability Hypothesis Density (PHD) filter. The emphasis in this thesis is on the further development of the PHD filter for handling more advanced target tracking problems, principally involving multiple group and extended targets. A group target is regarded as a collection of targets that share a common motion or characteristic, while an extended target is regarded as a target that potentially generates multiple measurements. The main contributions are the derivations of the PHD filter for multiple group and extended target tracking problems and their subsequent closed-form solutions. The proposed algorithms are applied in simulated scenarios and their estimate results demonstrate that accurate tracking performance is attainable for certain group/extended target tracking problems. The performance is further analysed with the use of suitable metrics.

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Pasha, Syed Electrical Engineering &amp Telecommunications Faculty of Engineering UNSW. « Problems in nonlinear Bayesian filtering ». Awarded by:University of New South Wales. Electrical Engineering & ; Telecommunications, 2009. http://handle.unsw.edu.au/1959.4/43792.

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This dissertation presents solutions to two open problems in estimation theory. The first is a tractable analytical solution for problems in multi-target filtering which are too complex to solve using traditional techniques. The second explores a new approach to the nonlinear filtering problem for a general class of models. The approach to the multi-target filtering problem which involves jointly estimating a random process of the number of targets and their state, developed using the probability hypothesis density (PHD) filter alleviates the intractability of the problem by avoiding explicit data association. Moreover, the notion of linear jump Markov systems is generalized to the multiple target case to accommodate births, deaths and switching dynamics to derive a closed form solution to the PHD recursion for this so-called linear Gaussian jump Markov multi-target model. The proposed solution is general enough to accommodate a broad class of practical problems which are deemed intractable using traditional techniques. Based on this closed form solution, an efficient method is developed for tracking multiple maneuvering targets that switch between multiple models without the need for gating, track initiation and termination, or clustering for extracting state estimates. The approach to the nonlinear filtering problem explores the framework of the virtual linear fractional transformation (LFT) model which localizes the nonlinearity to the feedback with a simple and sparse structure. The LFT is an exact representation for any differentiable nonlinear mapping and therefore amenable to a general class of problems. An alternative analytical approximation method is presented which avoids linearization of the state space model. The uncorrelated structure of the feedback connection gives of the state space model. The uncorrelated structure of the feedback connection gives better second-order moment approximation of the nonlinearly mapped variables. By arranging the unscented transform in the feedback, the prediction and estimation steps are derived in closed form. The proposed filters for the discrete-time model and continuous-time dynamics with sampled-data measurements respectively are shown to be robust under highly nonlinear and uncertain conditions where standard analytical approximation based filters diverge. Moreover, the LFT based filters are efficient for online implementation. In addition, the LFT framework is applied to extend the closed form solution of the PHD recursion to the nonlinear jump Markov multi-target model.

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Petetin, Yohan. « Algorithmes de restauration bayésienne mono- et multi-objets dans des modèles markoviens ». Phd thesis, Institut National des Télécommunications, 2013. http://tel.archives-ouvertes.fr/tel-00939083.

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Cette thèse est consacrée au problème d'estimation bayésienne pour le filtrage statistique, dont l'objectif est d'estimer récursivement des états inconnus à partir d'un historique d'observations, dans un modèle stochastique donné. Les modèles stochastiques considérés incluent principalement deux grandes classes de modèles : les modèles de Markov cachés et les modèles de Markov à sauts conditionnellement markoviens. Ici, le problème est abordé sous sa forme générale dans la mesure où nous considérons le problème du filtrage mono- et multi objet(s), ce dernier étant abordé sous l'angle de la théorie des ensembles statistiques finis et du filtre " Probability Hypothesis Density ". Tout d'abord, nous nous intéressons à l'importante classe d'approximations que constituent les algorithmes de Monte Carlo séquentiel, qui incluent les algorithmes d'échantillonnage d'importance séquentiel et de filtrage particulaire auxiliaire. Les boucles de propagation mises en jeux dans ces algorithmes sont étudiées et des algorithmes alternatifs sont proposés. Les algorithmes de filtrage particulaire dits " localement optimaux ", c'est à dire les algorithmes d'échantillonnage d'importance avec densité d'importance conditionnelle optimale et de filtrage particulaire auxiliaire pleinement adapté sont comparés statistiquement, en fonction des paramètres du modèle donné. Ensuite, les méthodes de réduction de variance basées sur le théorème de Rao-Blackwell sont exploitées dans le contexte du filtrage mono- et multi-objet(s) Ces méthodes, utilisées principalement en filtrage mono-objet lorsque la dimension du vecteur d'état à estimer est grande, sont dans un premier temps étendues pour les approximations Monte Carlo du filtre Probability Hypothesis Density. D'autre part, des méthodes de réduction de variance alternatives sont proposées : bien que toujours basées sur le théorème de Rao-Blackwell, elles ne se focalisent plus sur le caractère spatial du problème mais plutôt sur son caractère temporel. Enfin, nous abordons l'extension des modèles probabilistes classiquement utilisés. Nous rappelons tout d'abord les modèles de Markov couple et triplet dont l'intérêt est illustré à travers plusieurs exemples pratiques. Ensuite, nous traitons le problème de filtrage multi-objets, dans le contexte des ensembles statistiques finis, pour ces modèles. De plus, les propriétés statistiques plus générales des modèles triplet sont exploitées afin d'obtenir de nouvelles approximations de l'estimateur bayésien optimal (au sens de l'erreur quadratique moyenne) dans les modèles à sauts classiquement utilisés; ces approximations peuvent produire des estimateurs de performances comparables à celles des approximations particulaires, mais ont l'avantage d'être moins coûteuses sur le plan calculatoire

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Pace, Michele. « Stochastic models and methods for multi-object tracking ». Phd thesis, Université Sciences et Technologies - Bordeaux I, 2011. http://tel.archives-ouvertes.fr/tel-00651396.

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La poursuite multi-cibles a pour objet le suivi d'un ensemble de cibles mobiles à partir de données obtenues séquentiellement. Ce problème est particulièrement complexe du fait du nombre inconnu et variable de cibles, de la présence de bruit de mesure, de fausses alarmes, d'incertitude de détection et d'incertitude dans l'association de données. Les filtres PHD (Probability Hypothesis Density) constituent une nouvelle gamme de filtres adaptés à cette problématique. Ces techniques se distinguent des méthodes classiques (MHT, JPDAF, particulaire) par la modélisation de l'ensemble des cibles comme un ensemble fini aléatoire et par l'utilisation des moments de sa densité de probabilité. Dans la première partie, on s'intéresse principalement à la problématique de l'application des filtres PHD pour le filtrage multi-cibles maritime et aérien dans des scénarios réalistes et à l'étude des propriétés numériques de ces algorithmes. Dans la seconde partie, nous nous intéressons à l'étude théorique des processus de branchement liés aux équations du filtrage multi-cibles avec l'analyse des propriétés de stabilité et le comportement en temps long des semi-groupes d'intensités de branchements spatiaux. Ensuite, nous analysons les propriétés de stabilité exponentielle d'une classe d'équations à valeurs mesures que l'on rencontre dans le filtrage non-linéaire multi-cibles. Cette analyse s'applique notamment aux méthodes de type Monte Carlo séquentielles et aux algorithmes particulaires dans le cadre des filtres de Bernoulli et des filtres PHD.

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FANTACCI, CLAUDIO. « Distributed multi-object tracking over sensor networks : a random finite set approach ». Doctoral thesis, 2015. http://hdl.handle.net/2158/1003256.

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The aim of the present dissertation is to address distributed tracking over a network of heterogeneous and geographically dispersed nodes (or agents) with sensing, communication and processing capabilities. Tracking is carried out in the Bayesian framework and its extension to a distributed context is made possible via an information-theoretic approach to data fusion which exploits consensus algorithms and the notion of Kullback–Leibler Average (KLA) of the Probability Density Functions (PDFs) to be fused. The first step toward distributed tracking considers a single moving object. Consensus takes place in each agent for spreading information over the network so that each node can track the object. To achieve such a goal, consensus is carried out on the local single-object posterior distribution, which is the result of local data processing, in the Bayesian setting, exploiting the last available measurement about the object. Such an approach is called Consensus on Posteriors (CP). The first contribution of the present work is an improvement to the CP algorithm, namely Parallel Consensus on Likelihoods and Priors (CLCP). The idea is to carry out, in parallel, a separate consensus for the novel information (likelihoods) and one for the prior information (priors). This parallel procedure is conceived to avoid underweighting the novel information during the fusion steps. The outcomes of the two consensuses are then combined to provide the fused posterior density. Furthermore, the case of a single highly-maneuvering object is addressed. To this end, the object is modeled as a jump Markovian system and the multiple model (MM) filtering approach is adopted for local estimation. Thus, the consensus algorithms needs to be re-designed to cope with this new scenario. The second contribution has been to devise two novel consensus MM filters to be used for tracking a maneuvering object. The novel consensus-based MM filters are based on the First Order Generalized Pseudo-Bayesian (GPB1) and Interacting Multiple Model (IMM) filters. The next step is in the direction of distributed estimation of multiple moving objects. In order to model, in a rigorous and elegant way, a possibly time-varying number of objects present in a given area of interest, the Random Finite Set (RFS) formulation is adopted since it provides the notion of probability density for multi-object states that allows to directly extend existing tools in distributed estimation to multi-object tracking. The multi-object Bayes filter proposed by Mahler is a theoretically grounded solution to recursive Bayesian tracking based on RFSs. However, the multi-object Bayes recursion, unlike the single-object counterpart, is affected by combinatorial complexity and is, therefore, computationally infeasible except for very small-scale problems involving few objects and/or measurements. For this reason, the computationally tractable Probability Hypothesis Density (PHD) and Cardinalized PHD (CPHD) filtering approaches will be used as a first endeavour to distributed multiobject filtering. The third contribution is the generalisation of the single-object KLA to the RFS framework, which is the theoretical fundamental step for developing a novel consensus algorithm based on CPHD filtering, namely the Consensus CPHD (CCPHD). Each tracking agent locally updates multi-object CPHD, i.e. the cardinality distribution and the PHD, exploiting the multi-object dynamics and the available local measurements, exchanges such information with communicating agents and then carries out a fusion step to combine the information from all neighboring agents. The last theoretical step of the present dissertation is toward distributed filtering with the further requirement of unique object identities. To this end the labeled RFS framework is adopted as it provides a tractable approach to the multi-object Bayesian recursion. The δ- GLMB filter is an exact closed-form solution to the multi-object Bayes recursion which jointly yields state and label (or trajectory) estimates in the presence of clutter, misdetections and association uncertainty. Due to the presence of explicit data associations in the δ-GLMB filter, the number of components in the posterior grows without bound in time. The fourth contribution of this thesis is an efficient approximation of the δ-GLMB filter, namely Marginalized δ-GLMB (Mδ-GLMB), which preserves key summary statistics (i.e. both the PHD and cardinality distribution) of the full labeled posterior. This approximation also facilitates efficient multi-sensor tracking with detection-based measurements. Simulation results are presented to verify the proposed approach. Finally, distributed labeled multi-object tracking over sensor networks is taken into account. The last contribution is a further generalization of the KLA to the labeled RFS framework, which enables the development of two novel consensus tracking filters, namely the Consensus Marginalized δ-Generalized Labeled Multi-Bernoulli (CM-δGLMB) and the Consensus Labeled Multi-Bernoulli (CLMB) tracking filters. The proposed algorithms provide a fully distributed, scalable and computationally efficient solution for multi-object tracking. Simulation experiments on challenging single-object or multi-object tracking scenarios confirm the effectiveness of the proposed contributions.

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« Urban Terrain Multiple Target Tracking Using the Probability Hypothesis Density Particle Filter ». Master's thesis, 2011. http://hdl.handle.net/2286/R.I.9471.

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abstract: The tracking of multiple targets becomes more challenging in complex environments due to the additional degrees of nonlinearity in the measurement model. In urban terrain, for example, there are multiple reflection path measurements that need to be exploited since line-of-sight observations are not always available. Multiple target tracking in urban terrain environments is traditionally implemented using sequential Monte Carlo filtering algorithms and data association techniques. However, data association techniques can be computationally intensive and require very strict conditions for efficient performance. This thesis investigates the probability hypothesis density (PHD) method for tracking multiple targets in urban environments. The PHD is based on the theory of random finite sets and it is implemented using the particle filter. Unlike data association methods, it can be used to estimate the number of targets as well as their corresponding tracks. A modified maximum-likelihood version of the PHD (MPHD) is proposed to automatically and adaptively estimate the measurement types available at each time step. Specifically, the MPHD allows measurement-to-nonlinearity associations such that the best matched measurement can be used at each time step, resulting in improved radar coverage and scene visibility. Numerical simulations demonstrate the effectiveness of the MPHD in improving tracking performance, both for tracking multiple targets and targets in clutter.
Dissertation/Thesis
M.S. Electrical Engineering 2011

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Chapitres de livres sur le sujet "Cardinalized probability hypothesis density"

Wang, Ding, Xu Tang et Qun Wan. « The Recursive Spectral Bisection Probability Hypothesis Density Filter ». Dans Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, 47–56. Cham : Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-36402-1_5.

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Zhang, Pu, Hongwei Li et Yuan Huang. « Quadrature Kalman Probability Hypothesis Density Filter for Multi-Target Tracking ». Dans Informatics in Control, Automation and Robotics, 757–64. Berlin, Heidelberg : Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-25899-2_102.

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Wu, Tianjun, et Jianghong Ma. « Unscented Particle Implementation of Probability Hypothesis Density Filter for Multisensor Multitarget Tracking ». Dans Recent Advances in Computer Science and Information Engineering, 321–26. Berlin, Heidelberg : Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-25792-6_48.

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Rezatofighi, Seyed Hamid, Stephen Gould, Ba-Ngu Vo, Katarina Mele, William E. Hughes et Richard Hartley. « A Multiple Model Probability Hypothesis Density Tracker for Time-Lapse Cell Microscopy Sequences ». Dans Lecture Notes in Computer Science, 110–22. Berlin, Heidelberg : Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-38868-2_10.

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Atitey, Komlan, et Yan Cang. « A Novel Prediction Algorithm in Gaussian-Mixture Probability Hypothesis Density Filter for Target Tracking ». Dans Lecture Notes in Computer Science, 373–93. Cham : Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-21978-3_33.

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Zhu, Jihong, Benlian Xu, Fei Wang et Qiquan Wang. « A New Method Based on Ant Colony Optimization for the Probability Hypothesis Density Filter ». Dans Lecture Notes in Computer Science, 537–42. Berlin, Heidelberg : Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-21524-7_66.

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Chen, Jiandan, Iyeyinka Damilola Olayanju, Olabode Paul Ojelabi et Wlodek Kulesza. « RFID Multi-target Tracking Using the Probability Hypothesis Density Algorithm for a Health Care Application ». Dans Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, 95–105. Berlin, Heidelberg : Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-32304-1_9.

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Huang, Fangming, Kun Wang, Jian Xu et Zhiliang Huang. « The Analytic Implementation of the Multisensor Probability Hypothesis Density Filter ». Dans Multisensor Data Fusion, 235–52. CRC Press, 2017. http://dx.doi.org/10.1201/b18851-15.

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« Incorporating Uncertainty into Fishery Models ». Dans Incorporating Uncertainty into Fishery Models, sous la direction de Thomas E. Helser, Alexei Sharov et Desmond M. Kahn. American Fisheries Society, 2002. http://dx.doi.org/10.47886/9781888569315.ch6.

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Abstract.—Stock assessment methodology has increasingly employed statistical procedures as a means to incorporate uncertainty into assessment advice. Deterministic values of fishing mortality rates (Ft ) estimated from assessment models have been replaced by empirical distributions that can be compared with an appropriate biological reference point (FBRP) to generate statements of probability (e.g., Pr[Ft ≥ FBRP]) regarding the status of the resource. It must be recognized, however, that terminal year fishing mortality rates and the biological reference points to which they are compared are both estimated with error, which will impact the quality of decisions regarding the status of the stock. We propose a two-tier stochastic decision-based framework for a recently conducted stock assessment of the Delaware Bay blue crab stock that specifies not only the probability for the condition Pr(Ft ≥ FBRP), but also the statistical level of confidence (i.e., 90%) in that decision. The approach uses a mixed Monte Carlobootstrap procedure to estimate probability distributions for both the terminal year fishing mortality rate (Ft ) and the replacement fishing mortality rate, approximated by FMED as an overfishing definition. Probability density functions (PDFs) for Ft and FMED, generated using the mixed Monte Carlo-bootstrap procedure, show that recent fishing mortality rates (80% CI from 0.6 to 1.2) are generally below the FMED overfishing definition (80% CI from 0.9 to 1.6), with significant overlap in the PDFs. Using the PDFs, the stochastic decision-based approach then generates a probability profile by integrating the area under the Ft PDF for different decision confidence levels (e.g. 90%, 80%, 70%, etc.), which can be thought of as one-tailed α-probability from standard statistical hypothesis testing. For example, at the 80% decision confidence level (value of F corresponding to the upper 20% of the FMED PDF), Pr(Ft > FMED) is about 0.03. Thus, with high confidence (80%), we can state that the blue crab stock is not currently being overfished. This approach can be extended to decisions regarding control laws that specify both maximum fishing rate and minimum biomass thresholds.

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Actes de conférences sur le sujet "Cardinalized probability hypothesis density"

Danaee, Meysam R., et Fereidoon Behnia. « Auxiliary unscented particle cardinalized probability hypothesis density ». Dans 2013 21st Iranian Conference on Electrical Engineering (ICEE). IEEE, 2013. http://dx.doi.org/10.1109/iraniancee.2013.6599709.

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Georgescu, Ramona, et Peter Willett. « Multiple model cardinalized probability hypothesis density filter ». Dans SPIE Optical Engineering + Applications, sous la direction de Oliver E. Drummond. SPIE, 2011. http://dx.doi.org/10.1117/12.890953.

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Georgescu, Ramona, et Peter Willett. « Classification aided cardinalized probability hypothesis density filter ». Dans SPIE Defense, Security, and Sensing. SPIE, 2012. http://dx.doi.org/10.1117/12.917729.

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Wang, Yang, Zhongliang Jing et Shiqiang Hu. « Data Association for Cardinalized Probability Hypothesis Density Filter ». Dans 2009 Fourth International Conference on Innovative Computing, Information and Control (ICICIC). IEEE, 2009. http://dx.doi.org/10.1109/icicic.2009.153.

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Erdinc, Ozgur, Peter Willett et Yaakov Bar-Shalom. « A physical-space approach for the probability hypothesis density and cardinalized probability hypothesis density filters ». Dans Defense and Security Symposium, sous la direction de Oliver E. Drummond. SPIE, 2006. http://dx.doi.org/10.1117/12.673194.

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Reuter, Stephan, Daniel Meissner et Klaus Dietmayer. « Multi-object tracking at intersections using the cardinalized probability hypothesis density filter ». Dans 2012 15th International IEEE Conference on Intelligent Transportation Systems - (ITSC 2012). IEEE, 2012. http://dx.doi.org/10.1109/itsc.2012.6338787.

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Vo, Ba-tuong, Ba-ngu Vo et Antonio Cantoni. « The Cardinalized Probability Hypothesis Density Filter for Linear Gaussian Multi-Target Models ». Dans 2006 40th Annual Conference on Information Sciences and Systems. IEEE, 2006. http://dx.doi.org/10.1109/ciss.2006.286554.

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Hauschildt, Daniel. « Gaussian mixture implementation of the cardinalized probability hypothesis density filter for superpositional sensors ». Dans 2011 International Conference on Indoor Positioning and Indoor Navigation (IPIN). IEEE, 2011. http://dx.doi.org/10.1109/ipin.2011.6071936.

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Jones, Brandon A., Steve Gehly et Penina Axelrad. « Measurement-based Birth Model for a Space Object Cardinalized Probability Hypothesis Density Filter ». Dans AIAA/AAS Astrodynamics Specialist Conference. Reston, Virginia : American Institute of Aeronautics and Astronautics, 2014. http://dx.doi.org/10.2514/6.2014-4311.

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Lu, Zhejun, Weidong Hu, Yongxiang Liu et Thia Kirubaraian. « A new Cardinalized Probability Hypothesis Density Filter with Efficient Track Continuity and Extraction ». Dans 2018 21st International Conference on Information Fusion (FUSION 2018). IEEE, 2018. http://dx.doi.org/10.23919/icif.2018.8455589.

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