Gotowe bibliografie tematyczne / Continuous-Discrete filter

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  5. Streszczenia konferencji

Gotowa bibliografia na temat „Continuous-Discrete filter”

Autor: Grafiati
Data publikacji: 5 października 2024
Data aktualizacji: 19 lipca 2025

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Artykuły w czasopismach na temat "Continuous-Discrete filter"

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Lange, Theresa. "Derivation of ensemble Kalman–Bucy filters with unbounded nonlinear coefficients." Nonlinearity 35, no. 2 (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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Xia, Yuanqing, Zhihong Deng, Li Li, and Xiumei Geng. "A new continuous-discrete particle filter for continuous-discrete nonlinear systems." Information Sciences 242 (September 2013): 64–75. http://dx.doi.org/10.1016/j.ins.2013.04.030.

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Hu, Haoran, Shuxin Chen, Hao Wu, and Renke He. "Robust Estimation in Continuous–Discrete Cubature Kalman Filters for Bearings-Only Tracking." Applied Sciences 12, no. 16 (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 mea
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Sharma, Shambhu N., and H. Parthasarathy. "A two-body continuous-discrete filter." Nonlinear Dynamics 51, no. 1-2 (2007): 155–70. http://dx.doi.org/10.1007/s11071-007-9199-0.

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Chen, Xiuqiong, Jiayi Kang, and Stephen S. T. Yau. "Continuous discrete optimal transportation particle filter." Asian Journal of Mathematics 29, no. 1 (2025): 51–76. https://doi.org/10.4310/ajm.250429054646.

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Murata, Masaya, and Kaoru Hiramatsu. "Non-Gaussian Filter for Continuous-Discrete Models." IEEE Transactions on Automatic Control 64, no. 12 (2019): 5260–64. http://dx.doi.org/10.1109/tac.2019.2914953.

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Knudsen, Torben, and John Leth. "A New Continuous Discrete Unscented Kalman Filter." IEEE Transactions on Automatic Control 64, no. 5 (2019): 2198–205. http://dx.doi.org/10.1109/tac.2018.2867325.

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Chubich, Vladimir, and Svetlana Kulabukhova. "Research on the effectiveness of continuous-discrete cubature Kalman filter robust modifications." Information and Control Systems, no. 4 (August 24, 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
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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, no. 6 (2016): 878–93. http://dx.doi.org/10.1134/s1064230716050129.

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Weller, S. R., A. Feuer, G. C. Goodwin, and H. V. Poor. "Interrelations between continuous and discrete lattice filter structures." IEEE Transactions on Circuits and Systems II: Analog and Digital Signal Processing 40, no. 11 (1993): 705–13. http://dx.doi.org/10.1109/82.251838.

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Rozprawy doktorskie na temat "Continuous-Discrete filter"

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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
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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 aléatoi
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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 repres
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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 o
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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, and 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<br>國立臺灣大學<br>電機工程學系<br>86<br>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 impleme
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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
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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 t
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Książki na temat "Continuous-Discrete filter"

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H, Tranter William, and Fannin D. Ronald, eds. Signals and systems: Continuous and discrete. 4th ed. Prentice Hall, 1998.

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H, Tranter William, and Fannin D. Ronald, eds. Signals and systems: Continuous and discrete. 2nd ed. Macmillan, 1989.

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H, Tranter William, and Fannin D. Ronald, eds. Signals and systems: Continuous and discrete. 3rd ed. Macmillan, 1993.

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1935-, Srinath Mandyam D., ed. Continuous and discrete signals and systems. Prentice Hall, 1990.

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1935-, Srinath Mandyam D., ed. Continuous and discrete signals and systems. 2nd ed. Prentice Hall, 1998.

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1935-, Srinath Mandyam D., ed. Continuous and discrete signals and systems. Prentice-Hall, 1990.

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McGillem, Clare D. Continuous and discrete signal and system analysis. 3rd ed. Oxford University Press, 1991.

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McGillem, Clare D. Continuous and discrete signal and system analysis. 3rd ed. Saunders College Pub., 1991.

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

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Części książek na temat "Continuous-Discrete filter"

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

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Imran, A., X. Wang, and 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. 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, and Abderrahim Belmadani. "New Approach for Continuous and Discrete Optimization: Optimization by Morphological Filters." In Heuristics for Optimization and Learning. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-58930-1_28.

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Lancaster, Peter, and Leiba Rodman. "The Discrete Kalman Filter." In Algebraic Riccati Equations. 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. 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, and Shaival Hemant Nagarsheth. "The Universality of the Kalman Filter." In Advances in Data Mining and Database Management. 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., and N. K. Sinha. "A NONLINEAR FILTER FOR ESTIMATION OF STATES OF A CONTINUOUS-TIME SYSTEM WITH DISCRETE MEASUREMENTS." In Stochastic Control. 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. 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. Chapman and Hall/CRC, 2008. http://dx.doi.org/10.1201/9781420063646-10.

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

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Streszczenia konferencji na temat "Continuous-Discrete filter"

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Yang, Haibo, Dongmei Jiang, and Yu Zhu. "Nonlinear Tracking Using Uncorrelated Conversion Based Continuous-Discrete Filter." In 2024 IEEE International Conference on Unmanned Systems (ICUS). IEEE, 2024. https://doi.org/10.1109/icus61736.2024.10839842.

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Wen, Hao, Paul Krause, and Lee Gillam. "Continuous-Discrete Differentiable Particle Filters for Irregular Time Series." In ICASSP 2025 - 2025 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2025. https://doi.org/10.1109/icassp49660.2025.10889175.

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

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Xin, Li-Ping, Wen-Yue Shan, Xian-Duo Niu, and 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). IEEE, 2024. http://dx.doi.org/10.23919/ccc63176.2024.10661604.

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Yang, Tao, Henk A. P. Blom, and 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, and 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 distribu
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Lambert, Marc, Silvere Bonnabel, and 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, and 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, and 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, and 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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