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Literatura científica selecionada sobre o tema "Continuous-Discrete filter"

Autor: Grafiati

Publicado: 5 de outubro de 2024

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Artigos de revistas sobre o assunto "Continuous-Discrete filter"

Xia, Yuanqing, Zhihong Deng, Li Li e Xiumei Geng. "A new continuous-discrete particle filter for continuous-discrete nonlinear systems". Information Sciences 242 (setembro de 2013): 64–75. http://dx.doi.org/10.1016/j.ins.2013.04.030.

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Lange, Theresa. "Derivation of ensemble Kalman–Bucy filters with unbounded nonlinear coefficients". Nonlinearity 35, n.º 2 (31 de dezembro de 2021): 1061–92. http://dx.doi.org/10.1088/1361-6544/ac4337.

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Abstract We provide a rigorous derivation of the ensemble Kalman–Bucy filter as well as the ensemble transform Kalman–Bucy filter in case of nonlinear, unbounded model and observation operators. We identify them as the continuous time limit of the discrete-time ensemble Kalman filter and the ensemble square root filters, respectively, together with concrete convergence rates in terms of the discretisation step size. Simultaneously, we establish well-posedness as well as accuracy of both the continuous-time and the discrete-time filtering algorithms.

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Sharma, Shambhu N., e H. Parthasarathy. "A two-body continuous-discrete filter". Nonlinear Dynamics 51, n.º 1-2 (6 de fevereiro de 2007): 155–70. http://dx.doi.org/10.1007/s11071-007-9199-0.

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Hu, Haoran, Shuxin Chen, Hao Wu e Renke He. "Robust Estimation in Continuous–Discrete Cubature Kalman Filters for Bearings-Only Tracking". Applied Sciences 12, n.º 16 (15 de agosto de 2022): 8167. http://dx.doi.org/10.3390/app12168167.

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The model of bearings-only tracking is generally described by discrete–discrete filtering systems. Discrete robust methods are also frequently used to address measurement uncertainty problems in bearings-only tracking. The recently popular continuous–discrete filtering system considers the state model of the target to be continuous in time, and is more suitable for bearings-only tracking because of its higher mathematical solution accuracy. However, the sufficient evaluation of robust methods in continuous–discrete systems is not available. In addition, in the different continuous–discrete measurement environments, the choice of a robust algorithm also needs to be discussed. To fill this gap, this paper firstly establishes the continuous–discrete target tracking model, and then evaluates the performance of proposed robust square-root continuous–discrete cubature Kalman filter algorithms in the measurement of uncertainty problems. From the simulation results, the robust square-root continuous–discrete maximum correntropy cubature Kalman filter algorithm and the variational Bayesian square-root continuous–discrete cubature Kalman filter algorithm have better environmental adaptability, which provides a promising means for solving continuous–discrete robust problems.

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Murata, Masaya, e Kaoru Hiramatsu. "Non-Gaussian Filter for Continuous-Discrete Models". IEEE Transactions on Automatic Control 64, n.º 12 (dezembro de 2019): 5260–64. http://dx.doi.org/10.1109/tac.2019.2914953.

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Knudsen, Torben, e John Leth. "A New Continuous Discrete Unscented Kalman Filter". IEEE Transactions on Automatic Control 64, n.º 5 (maio de 2019): 2198–205. http://dx.doi.org/10.1109/tac.2018.2867325.

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Chubich, Vladimir, e Svetlana Kulabukhova. "Research on the effectiveness of continuous-discrete cubature Kalman filter robust modifications". Information and Control Systems, n.º 4 (24 de agosto de 2020): 11–19. http://dx.doi.org/10.31799/1684-8853-2020-4-11-19.

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Introduction: usually there are some outliers (abnormal measurements) in observed data, and they can significantly affect the quality of the data processing. Many dynamic processes are described with stochastic nonlinear equations. Modern nonlinear filters that include the cubature Kalman filter, which deserves a special attention, cannot effectively process data containing abnormal measurements. One of the possible solutions to this problem is to use so-called robust methods that have good performance when one has to analyze data containing outliers. The paper deals with the common situations, when the considered process is actually continuous, but the observed data is taken discretely. Purpose: identifying the most effective advanced robust modifications of the continuous-discrete cubature Kalman filter and giving the appropriate recommendations for their appliance. Results: four modifications of the continuous-discrete cubature Kalman filter have been proposed based on the variational Bayesian and correntropy robust approaches to parameter estimation for stochastic processes. All the modifications have parameters with optimal values depending on both the selected mathematical model and the considered set of observations composing the sample. These values are determined numerically by minimizing the accumulated root mean square error on some grid. The research on the effectiveness of the proposed robust modifications has been carried out for the problem of tracking a space vehicle during its reentry into the atmosphere. The stochastic and the grouped outliers have been considered. Two most effective filters that have approximately equal qualities of estimation have been derived. The correntropy filter that has one configurable parameter can be recommended for practical using.

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Rudenko, E. A. "Optimal continuous-discrete nonlinear finite memory filter with a discrete predictions". Journal of Computer and Systems Sciences International 55, n.º 6 (novembro de 2016): 878–93. http://dx.doi.org/10.1134/s1064230716050129.

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Weller, S. R., A. Feuer, G. C. Goodwin e H. V. Poor. "Interrelations between continuous and discrete lattice filter structures". IEEE Transactions on Circuits and Systems II: Analog and Digital Signal Processing 40, n.º 11 (1993): 705–13. http://dx.doi.org/10.1109/82.251838.

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Shald, Scott. "The continuous kalman filter as the limit of the discrete kalman filter". Stochastic Analysis and Applications 17, n.º 5 (janeiro de 1999): 841–56. http://dx.doi.org/10.1080/07362999908809638.

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Mais fontes

Teses / dissertações sobre o assunto "Continuous-Discrete filter"

Allahyani, Seham. "Contributions to filtering under randomly delayed observations and additive-multiplicative noise". Thesis, Brunel University, 2017. http://bura.brunel.ac.uk/handle/2438/16297.

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This thesis deals with the estimation of unobserved variables or states from a time series of noisy observations. Approximate minimum variance filters for a class of discrete time systems with both additive and multiplicative noise, where the measurement might be delayed randomly by one or more sample times, are investigated. The delayed observations are modelled by up to N sample times by using N Bernoulli random variables with values of 0 or 1. We seek to minimize variance over a class of filters which are linear in the current measurement (although potentially nonlinear in past measurements) and present a closed-form solution. An interpretation of the multiplicative noise in both transition and measurement equations in terms of filtering under additive noise and stochastic perturbations in the parameters of the state space system is also provided. This filtering algorithm extends to the case when the system has continuous time state dynamics and discrete time state measurements. The Euler scheme is used to transform the process into a discrete time state space system in which the state dynamics have a smaller sampling time than the measurement sampling time. The number of sample times by which the observation is delayed is considered to be uncertain and a fraction of the measurement sample time. The same problem is considered for nonlinear state space models of discrete time systems, where the measurement might be delayed randomly by one sample time. The linearisation error is modelled as an additional source of noise which is multiplicative in nature. The algorithms developed are demonstrated throughout with simulated examples.

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Iufereva, Olga. "Algorithmes de filtrage avec les observations distribuées par Poisson". Electronic Thesis or Diss., Université de Toulouse (2023-....), 2024. https://theses.hal.science/tel-04720020.

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La théorie du filtrage concerne essentiellement l’estimation optimale de l’état dans les systèmes stochastiques, surtout avec des mesures partielles et bruitées. Ce domaine, fortement lié à la théorie du contrôle, se concentre sur la synthèse d'estimateurs effectuant des calculs en temps réel pour minimiser l'erreur quadratique moyenne. La nécessité de telles estimations devient de plus en plus critique avec la prolifération de systèmes contrôlés par réseau, tels que les véhicules autonomes et les processus industriels complexes, où les processus d'observations sont soumis au caractère aléatoire de la transmission, ce qui donne lieu à des modèles d'information variables pour résoudre le problème d'estimation.Cette thèse aborde la tâche importante de l'estimation d'état dans les systèmes dynamiques stochastiques en temps continu lorsque les mesures sont disponible aux certains instants discrets defini par un processus aléatoire. En adaptant les méthodes d'estimation classiques, nous développons des équations pour un estimateur optimal d'état, explorons leurs propriétés et les aspect pratiques, et proposons et analysons des alternatives sous-optimales, présentant des parallèles avec les techniques existantes dans le domaine d'estimation classique lorsqu'elles sont appliquées aux processus d'observation Poisson-distribués.L'étude couvre trois classes de modèles mathématiques pour le système dynamique en temps continu et le processus d'observation discret. Tout d’abord, nous considérons des équations différentielles Ito-stochastiques avec le champ de vecteur Lipschitz et un coefficient de diffusion constant, alors que le processus d’observation discrète de dimension inférieure comprend la fonction nonlinéaire de l’état et un bruit Gaussien additif. Nous proposons des estimateurs d’état sous-optimaux continus-discrets, qui sont faciles à implémenter pour cette classe de systèmes. En supposant qu'un compteur de Poisson décrit les instants discrets auxquels les observations sont disponibles, nous calculons le processus de covariance d’erreur d’estimation. L'analyse est effectuée pour fournir les conditions de limitation du processus de covariance d'erreur, ainsi que la dépendance au taux d'échantillonnage moyen.Deuxièmement, nous considérons les systèmes dynamiques décrits par des chaînes de Markov en temps continu avec un espace d'état fini, et le processus d'observation est obtenu en discrétisant un processus stochastique conventionnel piloté par un processus de Wiener. Dans ce cas, nous montrons la convergence $L_1$ de l'estimateur optimal vers l'estimateur optimal classique (purement continu) (filtre de Wonham) quand l'intensité des processus de Poisson augmente.Enfin, nous étudions les filtres à particules continus-discrets pour les processus d'Ornstein-Uhlenbeck avec des observations discrètes décrites par des fonctions d'état linéaires et un bruit Gaussien additif. Les filtres à particules ont gagné beaucoup d'intérêt pour l'estimation d'état dans les modèles à grande échelle avec des mesures bruitées où le calcul du gain optimal est soit coûteux en calcul, soit pas entièrement réalisable en raison de la complexité de la dynamique. Dans cette thèse, nous proposons des processus de diffusion de type McKean–Vlasov continus-discrets, qui servent de modèle de champ moyen pour décrire la dynamique des particules. Nous étudions plusieurs types de processus de champ moyen en fonction de la manière dont les termes de bruit sont inclus pour l'imitation du processus d'état et du modèle d'observation. Les particules résultantes sont couplées via des covariances empiriques qui sont mises à jour en temps discrets avec l'arrivée de nouvelles observations. Avec une analyse appropriée des premier et deuxième instants, nous montrons que sous certaines conditions sur les paramètres du système, les performances des filtres à particules se rapprochent du filtre optimal quand le nombre de particules augmente
Filtering theory basically relates to optimal state estimation in stochastic dynamical systems, particularly when faced with partial and noisy data. This field, closely intertwined with control theory, focuses on designing estimators doing real-time computation while maintaining an acceptable level of accuracy as measured by the mean square error. The necessity for such estimates becomes increasingly critical with the proliferation of network-controlled systems, such as autonomous vehicles and complex industrial processes, where the observation processes are subject to randomness in transmission and this gives rise to varying information patterns under which the estimation must be carried out.This thesis addresses the important task of state estimation in continuous-time stochastic dynamical systems when the observation process is available only at some discrete time instants governed by a random process. By adapting classical estimation methods, we derive equations for optimal state estimator, explore their properties and practicality, and propose and evaluates sub-optimal alternatives, showcasing parallels to the existing techniques within the classical estimation domain when applied to Poisson-distributed observation processes.The study covers three classes of mathematical models for the continuous-time dynamical system and the discrete observation process. First, we consider Ito-stochastic differential equations with Lipschitz drift terms and constant diffusion coefficient, whereas the lower-dimensional discrete observation process comprises the nonlinear mapping of the state and additive Gaussian noise. We propose easy-to-implement continuous-discrete suboptimal state estimators for this system class. Assuming that a Poisson counter governs discrete times at which the observations are available, we compute the expectation or error covariance process. Analysis is carried out to provide conditions for boundedness of the error covariance process, as well as, the dependence on the mean sampling rate.Secondly, we consider the dynamical systems described by continuous-time Markov chains with finite state space, and the observation process is obtained by discretizing a conventional stochastic process driven by a Wiener process. For this case, the $L_1$-convergence of the derived optimal estimator to the classical (purely continuous) optimal estimator (Wonham filter) is shown with respect to increasing intensity of Poisson processes.Lastly, we study continuous-discrete particle filters for Ornstein-Uhlenbeck processes with discrete observations described by linear functions of state and additive Gaussian noise. Particle filters have gained a lot of interest for state estimation in large-scale models with noisy measurements where the computation of optimal gain is either computationally expensive or not entirely feasible due to complexity of the dynamics. In this thesis, we propose continuous-discrete McKean–Vlasov type diffusion processes, which serve as the mean-field model for describing the particle dynamics. We study several kinds of mean-field processes depending on how the noise terms are included in mimicking the state process and the observation model. The resulting particles are coupled through empirical covariances which are updated at discrete times with the arrival of new observations. With appropriate analysis of the first and second moments, we show that under certain conditions on system parameters, the performance of the particle filters approaches the optimal filter as the number of particles gets larger

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Boizot, Nicolas. "Adaptative high-gain extended Kalman filter and applications". Phd thesis, Université de Bourgogne, 2010. http://tel.archives-ouvertes.fr/tel-00559107.

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The work concerns the "observability problem"--the reconstruction of a dynamic process's full state from a partially measured state-- for nonlinear dynamic systems. The Extended Kalman Filter (EKF) is a widely-used observer for such nonlinear systems. However it suffers from a lack of theoretical justifications and displays poor performance when the estimated state is far from the real state, e.g. due to large perturbations, a poor initial state estimate, etc. . . We propose a solution to these problems, the Adaptive High-Gain (EKF). Observability theory reveals the existence of special representations characterizing nonlinear systems having the observability property. Such representations are called observability normal forms. A EKF variant based on the usage of a single scalar parameter, combined with an observability normal form, leads to an observer, the High-Gain EKF, with improved performance when the estimated state is far from the actual state. Its convergence for any initial estimated state is proven. Unfortunately, and contrary to the EKF, this latter observer is very sensitive to measurement noise. Our observer combines the behaviors of the EKF and of the high-gain EKF. Our aim is to take advantage of both efficiency with respect to noise smoothing and reactivity to large estimation errors. In order to achieve this, the parameter that is the heart of the high-gain technique is made adaptive. Voila, the Adaptive High-Gain EKF. A measure of the quality of the estimation is needed in order to drive the adaptation. We propose such an index and prove the relevance of its usage. We provide a proof of convergence for the resulting observer, and the final algorithm is demonstrated via both simulations and a real-time implementation. Finally, extensions to multiple output and to continuous-discrete systems are given.

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Havlíček, Martin. "Zkoumání konektivity mozkových sítí pomocí hemodynamického modelování". Doctoral thesis, Vysoké učení technické v Brně. Fakulta elektrotechniky a komunikačních technologií, 2012. http://www.nusl.cz/ntk/nusl-233576.

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Zobrazení funkční magnetickou rezonancí (fMRI) využívající "blood-oxygen-level-dependent" efekt jako indikátor lokální aktivity je velmi užitečnou technikou k identifikaci oblastí mozku, které jsou aktivní během percepce, kognice, akce, ale také během klidového stavu. V poslední době také roste zájem o studium konektivity mezi těmito oblastmi, zejména v klidovém stavu. Tato práce předkládá nový a originální přístup k problému nepřímého vztahu mezi měřenou hemodynamickou odezvou a její příčinou, tj. neuronálním signálem. Zmíněný nepřímý vztah komplikuje odhad efektivní konektivity (kauzálního ovlivnění) mezi různými oblastmi mozku z dat fMRI. Novost prezentovaného přístupu spočívá v použití (zobecněné nelineární) techniky slepé dekonvoluce, což dovoluje odhad endogenních neuronálních signálů (tj. vstupů systému) z naměřených hemodynamických odezev (tj. výstupů systému). To znamená, že metoda umožňuje "data-driven" hodnocení efektivní konektivity na neuronální úrovni i v případě, že jsou měřeny pouze zašumělé hemodynamické odezvy. Řešení tohoto obtížného dekonvolučního (inverzního) problému je dosaženo za použití techniky nelineárního rekurzivního Bayesovského odhadu, který poskytuje společný odhad neznámých stavů a parametrů modelu. Práce je rozdělena do tří hlavních částí. První část navrhuje metodu k řešení výše uvedeného problému. Metoda využívá odmocninové formy nelineárního kubaturního Kalmanova filtru a kubaturního Rauch-Tung-Striebelova vyhlazovače, ovšem rozšířených pro účely řešení tzv. problému společného odhadu, který je definován jako simultánní odhad stavů a parametrů sekvenčním přístupem. Metoda je navržena především pro spojitě-diskrétní systémy a dosahuje přesného a stabilního řešení diskretizace modelu kombinací nelineárního (kubaturního) filtru s metodou lokální linearizace. Tato inverzní metoda je navíc doplněna adaptivním odhadem statistiky šumu měření a šumů procesu (tj. šumů neznámých stavů a parametrů). První část práce je zaměřena na inverzi modelu pouze jednoho časového průběhu; tj. na odhad neuronální aktivity z fMRI signálu. Druhá část generalizuje navrhovaný přístup a aplikuje jej na více časových průběhů za účelem umožnění odhadu parametrů propojení neuronálního modelu interakce; tj. odhadu efektivní konektivity. Tato metoda představuje inovační stochastické pojetí dynamického kauzálního modelování, což ji činí odlišnou od dříve představených přístupů. Druhá část se rovněž zabývá metodami Bayesovského výběru modelu a navrhuje techniku pro detekci irelevantních parametrů propojení za účelem dosažení zlepšeného odhadu parametrů. Konečně třetí část se věnuje ověření navrhovaného přístupu s využitím jak simulovaných tak empirických fMRI dat, a je významných důkazem o velmi uspokojivých výsledcích navrhovaného přístupu.

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Ondra, Josef. "Komprese signálů EKG s využitím vlnkové transformace". Master's thesis, Vysoké učení technické v Brně. Fakulta elektrotechniky a komunikačních technologií, 2008. http://www.nusl.cz/ntk/nusl-217209.

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Signal compression is daily-used tool for memory capacities reduction and for fast data communication. Methods based on wavelet transform seem to be very effective nowadays. Signal decomposition with a suitable bank filters following with coefficients quantization represents one of the available technique. After packing quantized coefficients into one sequence, run length coding together with Huffman coding are implemented. This thesis focuses on compression effectiveness for the different wavelet transform and quantization settings.

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Tang, Ding-Chiang, e 唐鼎強. "Optimal design of 2-D digital filters and 1-D filter banks with continuous and discrete coefficients". Thesis, 1998. http://ndltd.ncl.edu.tw/handle/15121517814866567712.

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碩士
國立臺灣大學
電機工程學系
86
Recently, digital filters and digital filter banks have played an important role on many digital signal processing theories and applications. In this thesis, we apply weighted least squares(WLS) algorithm and Karmarkar's algorithm as optimization tools to design 1-D Nonuniform-Division Maximally Decimated Filter Banks(NDMDFB) and 2-D FIR filters with continuous coefficients. Due to the circuit complexity and high cost of multibit multipliers while implementing an FIR filter of the conventional structure, we propose a new FIR filter structure whose main part consists of a transversal filter with tap coefficients restricted to -1,0,+1 and cascaded with a simple recursive section with some specific resetting function. Therefore, it's unnecessary for transversal filter to use multipliers, the configuration is suitable for hardware implementation. Based on the new structure, we design NDMDFB and 2-D FIR filter with coefficients -1,0,and +1. Design examples show the effectiveness of the proposed design technique in the thesis.

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Boddikurapati, Sirish. "Sequential Monte Carlo Methods With Applications To Communication Channels". 2009. http://hdl.handle.net/1969.1/ETD-TAMU-2009-12-7537.

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Estimating the state of a system from noisy measurements is a problem which arises in a variety of scientific and industrial areas which include signal processing, communications, statistics and econometrics. Recursive filtering is one way to achieve this by incorporating noisy observations as they become available with prior knowledge of the system model. Bayesian methods provide a general framework for dynamic state estimation problems. The central idea behind this recursive Bayesian estimation is computing the probability density function of the state vector of the system conditioned on the measurements. However, the optimal solution to this problem is often intractable because it requires high-dimensional integration. Although we can use the Kalman lter in the case of a linear state space model with Gaussian noise, this method is not optimum for a non-linear and non-Gaussian system model. There are many new methods of filtering for the general case. The main emphasis of this thesis is on one such recently developed filter, the particle lter [2,3,6]. In this thesis, a detailed introduction to particle filters is provided as well as some guidelines for the efficient implementation of the particle lter. The application of particle lters to various communication channels like detection of symbols over the channels, capacity calculation of the channel are discussed.

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Wang, Yan. "Design techniques for wideband low-power Delta-Sigma analog-to-digital converters". Thesis, 2009. http://hdl.handle.net/1957/13664.

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Delta-Sigma (ΔΣ) analog-to-digital converters (ADCs) are traditionally used in high quality audio systems, instrumentation and measurement (I&M) and biomedical devices. With the continued downscaling of CMOS technology, they are becoming popular in wideband applications such as wireless and wired communication systems,high-definition television and radar systems. There are two general realizations of a ΔΣ modulator. One is based on the discrete-time (DT) switched-capacitor (SC) circuitry and the other employs continuous-time (CT) circuitry. Compared to a CT structure, the DT ΔΣ ADC is easier to analyze and design, is more robust to process variations and jitter noise, and is more flexible in the multi-mode applications. On the other hand, the CT ΔΣ ADC does not suffer from the strict settling accuracy requirement for the loop filter and thus can achieve lower power dissipation and higher sampling frequency than its DT counterpart. In this thesis, both DT and CT ΔΣ ADCs are investigated. Several design innovations, in both system-level and circuit-level, are proposed to achieve lower power consumption and wider signal bandwidth. For DT ΔΣ ADCs, a new dynamic-biasing scheme is proposed to reduce opamp bias current and the associated signal-dependent harmonic distortion is minimized by using the low-distortion architecture. The technique was verified in a 2.5MHz BW and 13bit dynamic range DT ΔΣ ADC. In addition, a second-order noise coupling technique is presented to save two integrators for the loop filter, and to achieve low power dissipation. Also, a direct-charge-transfer (DCT) technique is suggested to reduce the speed requirements of the adder, which is also preferable in wideband low-power applications. For CT ΔΣ ADCs, a wideband low power CT 2-2 MASH has been designed. High linearity performance was achieved by using a modified low-distortion technique, and the modulator achieves higher noise-shaping ability than the single stage structure due to the inter-stage gain. Also, the quantization noise leakage due to analog circuit non-idealities can be adaptively compensated by a designed digital calibration filter. Using a 90nm process, simulation of the modulator predicts a 12bit resolution within 20MHz BW and consumes only 25mW for analog circuitry. In addition, the noise-coupling technique is investigated and proposed for the design of CT ΔΣ ADCs and it is promising to achieve low power dissipation for wideband applications. Finally, the application of noise-coupling technique is extended and introduced to high-accuracy incremental data converters. Low power dissipation can be expected.
Graduation date: 2010

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Livros sobre o assunto "Continuous-Discrete filter"

H, Tranter William, e Fannin D. Ronald, eds. Signals and systems: Continuous and discrete. 4^a ed. Upper Saddle River, NJ: Prentice Hall, 1998.

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H, Tranter William, e Fannin D. Ronald, eds. Signals and systems: Continuous and discrete. 2^a ed. New York: Macmillan, 1989.

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H, Tranter William, e Fannin D. Ronald, eds. Signals and systems: Continuous and discrete. 3^a ed. New York: Macmillan, 1993.

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1935-, Srinath Mandyam D., ed. Continuous and discrete signals and systems. Englewood Cliffs, N.J: Prentice Hall, 1990.

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1935-, Srinath Mandyam D., ed. Continuous and discrete signals and systems. 2^a ed. Upper Saddle River, NJ: Prentice Hall, 1998.

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1935-, Srinath Mandyam D., ed. Continuous and discrete signals and systems. Englewood Cliffs,N.J: Prentice-Hall, 1990.

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McGillem, Clare D. Continuous and discrete signal and system analysis. 3^a ed. New York: Oxford University Press, 1991.

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McGillem, Clare D. Continuous and discrete signal and system analysis. 3^a ed. Philadelphia: Saunders College Pub., 1991.

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Tranter, William H., Rodger E. Ziemer e D. R. Fannin. Signals and Systems : Pearson New International Edition: Continuous and Discrete. Pearson Education, Limited, 2013.

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Capítulos de livros sobre o assunto "Continuous-Discrete filter"

Moschytz, George S. "From Continuous Time to Discrete Time". In Analog Circuit Theory and Filter Design in the Digital World, 369–80. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-00096-7_14.

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Imran, A., X. Wang e X. Yue. "State and Covariance Matrix Propagation for Continuous-Discrete Extended Kalman Filter Using Modified Chebyshev Picard Iteration Method". In Computational and Experimental Simulations in Engineering, 141–49. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-02097-1_11.

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Khelifa, Chahinez Nour El Houda, e Abderrahim Belmadani. "New Approach for Continuous and Discrete Optimization: Optimization by Morphological Filters". In Heuristics for Optimization and Learning, 425–40. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-58930-1_28.

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Lancaster, Peter, e Leiba Rodman. "The Discrete Kalman Filter". In Algebraic Riccati Equations, 371–86. Oxford University PressOxford, 1995. http://dx.doi.org/10.1093/oso/9780198537953.003.0017.

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Abstract One of the more important motivations for the study of algebraic Riccati equations was recognized in the early 1960s after publication of the works of Kalman (1960a) and Kalman and Bucy (1961) on filtering and prediction problems of discrete and continuous types, respectively. A full development of the theory behind these processes is beyond the scope of this book. However, this chapter is devoted to an intuitive development of the ideas behind discrete Kalman filtering and, in Section 17.5, an account of the basic convergence theorem for the Riccati difference equation. This may be all that the reader needs to know about the problem, or it may lead the way to more thorough treatments such as the careful mathematical development of Catlin (1989) or the graduate engineering text of Anderson and Moore (1979).

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Berber, Stevan. "Sampling and Reconstruction of Continuous-Time Signals". In Discrete Communication Systems, 674–89. Oxford University Press, 2021. http://dx.doi.org/10.1093/oso/9780198860792.003.0013.

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This chapter presents the theory for transferring a continuous-time signal into its discrete-time form by sampling, and then converting the obtained samples to a digital signal suitable for processing in a processing machine, using the procedure of sample quantizing and coding. Then, the procedure of converting a digitally processed signal into discrete signal samples and the reconstruction of the initial continuous-time signal via a lowpass reconstruction filter is presented. The theory provides the mathematical base for both analogue-to-digital and digital-to-analogue conversions, which are extensively used for processing signals in discrete communication systems. The chapter goes on to show that the Nyquist criterion must be fulfilled to eliminate signal aliasing in the frequency domain. Finally, the mathematical model for transferring a continuous-time signal into its discrete-time form, and vice versa, is presented and demonstrated for a sinusoidal signal.

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Rathore, Sandhya, Shambhu Nath Sharma e Shaival Hemant Nagarsheth. "The Universality of the Kalman Filter". In Advances in Data Mining and Database Management, 277–94. IGI Global, 2021. http://dx.doi.org/10.4018/978-1-7998-4706-9.ch011.

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The universality of the Kalman filtering can be found in the control theory. The Kalman filter has found its applications in sophisticated autonomous systems and smart products, which are attributed to its realization in a single complex chip. In this chapter, considering the Kalman filter from the perspective of conditional characteristic function evolution and Itô calculus, three Kalman filtering theorems and their formal proof are developed. Most notably, this chapter reveals the following: (1) Kalman filtering equations are a consequence of the ‘evolution of conditional characteristic function' for the linear stochastic differential system coupled with the linear discrete measurement system. (2) The Kalman filtering is a consequence of the ‘stochastic evolution of conditional characteristic function' for the linear stochastic differential system coupled with the linear continuous measurement system. (3) The structure of the Kalman filter remains invariant under two popular stochastic interpretations, the Itô vs Stratonovich.

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Raol, J. R., e N. K. Sinha. "A NONLINEAR FILTER FOR ESTIMATION OF STATES OF A CONTINUOUS-TIME SYSTEM WITH DISCRETE MEASUREMENTS". In Stochastic Control, 43–48. Elsevier, 1987. http://dx.doi.org/10.1016/b978-0-08-033452-3.50011-9.

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Jovanovic Dolecek, Gordana. "Digital Filters". In Encyclopedia of Multimedia Technology and Networking, Second Edition, 364–72. IGI Global, 2009. http://dx.doi.org/10.4018/978-1-60566-014-1.ch050.

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A signal is defined as any physical quantity that varies with changes of one or more independent variables, and each can be any physical value, such as time, distance, position, temperature, or pressure (Elali, 2003; Smith, 2002). The independent variable is usually referred to as “time”. Examples of signals that we frequently encounter are speech, music, picture, and video signals. If the independent variable is continuous, the signal is called continuous-time signal or analog signal, and is mathematically denoted as x(t). For discrete-time signals, the independent variable is a discrete variable; therefore, a discrete-time signal is defined as a function of an independent variable n, where n is an integer. Consequently, x(n) represents a sequence of values, some of which can be zeros, for each value of integer n. The discrete–time signal is not defined at instants between integers, and it is incorrect to say that x(n) is zero at times between integers. The amplitude of both the continuous and discrete-time signals may be continuous or discrete. Digital signals are discrete-time signals for which the amplitude is discrete. Figure 1 illustrates the analog and the discrete-time signals. Most signals that we encounter are generated by natural means. However, a signal can also be generated synthetically or by computer simulation (Mitra, 2006). Signal carries information, and the objective of signal processing is to extract useful information carried by the signal. The method of information extraction depends on the type of signal and the nature of the information being carried by the signal. “Thus, roughly speaking, signal processing is concerned with the mathematical representation of the signal and algorithmic operation carried out on it to extract the information present,’’ (Mitra, 2006, pp. 1).

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Chu, Eleanor. "Applications of the DFT in Digital Filtering and Filters". In Discrete and Continuous Fourier Transforms, 291–302. Chapman and Hall/CRC, 2008. http://dx.doi.org/10.1201/9781420063646-10.

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Bullock, T. E., e M. J. Moorman. "Extended Kalman Filters 1: Continuous and Discrete Linearizations". In Approximate Kalman Filtering, 3–8. WORLD SCIENTIFIC, 1993. http://dx.doi.org/10.1142/9789814317399_0001.

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Trabalhos de conferências sobre o assunto "Continuous-Discrete filter"

Brunke, Lukas, Siqi Zhou, Mingxuan Che e Angela P. Schoellig. "Practical Considerations for Discrete-Time Implementations of Continuous-Time Control Barrier Function-Based Safety Filters". In 2024 American Control Conference (ACC), 272–78. IEEE, 2024. http://dx.doi.org/10.23919/acc60939.2024.10644713.

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Xin, Li-Ping, Wen-Yue Shan, Xian-Duo Niu e Jia-Shuo Liu. "Adaptive fuzzy command filtered discrete control for cascade continuous stirred tank reactors with input constraint". In 2024 43rd Chinese Control Conference (CCC), 721–26. IEEE, 2024. http://dx.doi.org/10.23919/ccc63176.2024.10661604.

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Yang, Tao, Henk A. P. Blom e Prashant G. Mehta. "The continuous-discrete time feedback particle filter". In 2014 American Control Conference - ACC 2014. IEEE, 2014. http://dx.doi.org/10.1109/acc.2014.6859259.

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Ates, Abdullah, e YangQuan Chen. "Fractional Order Filter Discretization With Marine Predators Algorithm". In ASME 2021 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. American Society of Mechanical Engineers, 2021. http://dx.doi.org/10.1115/detc2021-67611.

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Abstract In this study, discrete time models of continuous time fractional order filters are obtained by using the Marine Predators Algorithm (MPA). Marine Predators optimization algorithm is a population-based heuristic method. This method is inspired by the hunting behavior of marine predators. The algorithm works on three basic phases. These phases occur according to the difference or equality of the velocity of the prey and the predator. As it is known, uniform distribution is generally used in stochastic based optimization algorithms. However, in the MPA method, Brownian and Levy distributions are also used as well as uniform distribution. First, continuous time frequency responses of fractional order filters are generated. Then, fourth order discrete time filters are designed that can give similar responses with generated continues time filter frequency responses. Ten parameters were optimized for the design of fourth order discrete time filters numerator and denominator. The Marine Predators method’s results are compared with the results of the Fractional order Darwinian Particle Swarm Optimization (FODPSO) algorithm, from which discrete time filters are obtained for two fractional order continuous time filter models. In this way, it has been shown comparatively that the Marine Predators Algorithm can be used in real engineering problems and can do filter discretization better.

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Lambert, Marc, Silvere Bonnabel e Francis Bach. "The continuous-discrete variational Kalman filter (CD-VKF)". In 2022 IEEE 61st Conference on Decision and Control (CDC). IEEE, 2022. http://dx.doi.org/10.1109/cdc51059.2022.9992993.

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Murata, Masaya, Isao Kawano e Koichi Inoue. "Ensemble Kalman Filter for Continuous-Discrete State-Space Models". In 2021 60th IEEE Conference on Decision and Control (CDC). IEEE, 2021. http://dx.doi.org/10.1109/cdc45484.2021.9682835.

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Wang, Tingjun, Haoran Cui e Xiaoxu Wang. "Variational Compensation Based Nonlinear Filter for Continuous-Discrete Stochastic Systems". In 2020 IEEE 23rd International Conference on Information Fusion (FUSION). IEEE, 2020. http://dx.doi.org/10.23919/fusion45008.2020.9190435.

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Miller, Gregory, Alexey Pankov e Konstantin Siemenikhin. "Minimax filter for statistically uncertain stochastic discrete-continuous linear system". In European Control Conference 2007 (ECC). IEEE, 2007. http://dx.doi.org/10.23919/ecc.2007.7068493.

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Herjolfsson, Gisli, Anna Hauksdottir e Sven Sigurosson. "Closed form Expressions of Linear Continuous-and Discrete-Time Filter Responses". In Proceedings of the 7th Nordic Signal Processing Symposium - NORSIG 2006. IEEE, 2006. http://dx.doi.org/10.1109/norsig.2006.275253.

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Shin, Vladimir, Du Yong Kim, Georgy Shevlyakov e Kiseon Kim. "A Suboptimal Filter for Continuous-Discrete Linear Systems with Parametric Uncertainties". In TENCON 2006 - 2006 IEEE Region 10 Conference. IEEE, 2006. http://dx.doi.org/10.1109/tencon.2006.343997.

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