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| Author | : Y. Yavin |
| Publisher | : Springer |
| Total Pages | : 276 |
| Release | : 2014-03-12 |
| Genre | : Technology & Engineering |
| ISBN | : 9783662185841 |
| Author | : Yaakov Yavin |
| Publisher | : Springer |
| Total Pages | : 290 |
| Release | : 1985 |
| Genre | : Mathematics |
| ISBN | : |
| Author | : Hisashi Tanizaki |
| Publisher | : Springer Science & Business Media |
| Total Pages | : 215 |
| Release | : 2013-11-11 |
| Genre | : Business & Economics |
| ISBN | : 366222237X |
For a nonlinear filtering problem, the most heuristic and easiest approximation is to use the Taylor series expansion and apply the conventional linear recursive Kalman filter algorithm directly to the linearized nonlinear measurement and transition equations. First, it is discussed that the Taylor series expansion approach gives us the biased estimators. Next, a Monte-Carlo simulation filter is proposed, where each expectation of the nonlinear functions is evaluated generating random draws. It is shown from Monte-Carlo experiments that the Monte-Carlo simulation filter yields the unbiased but inefficient estimator. Anotherapproach to the nonlinear filtering problem is to approximate the underlyingdensity functions of the state vector. In this monograph, a nonlinear and nonnormal filter is proposed by utilizing Monte-Carlo integration, in which a recursive algorithm of the weighting functions is derived. The densityapproximation approach gives us an asymptotically unbiased estimator. Moreover, in terms of programming and computational time, the nonlinear filter using Monte-Carlo integration can be easily extended to higher dimensional cases, compared with Kitagawa's nonlinear filter using numericalintegration.
| Author | : Kumar Pakki Bharani Chandra |
| Publisher | : Springer |
| Total Pages | : 184 |
| Release | : 2018-11-20 |
| Genre | : Technology & Engineering |
| ISBN | : 3030017974 |
This book gives readers in-depth know-how on methods of state estimation for nonlinear control systems. It starts with an introduction to dynamic control systems and system states and a brief description of the Kalman filter. In the following chapters, various state estimation techniques for nonlinear systems are discussed, including the extended, unscented and cubature Kalman filters. The cubature Kalman filter and its variants are introduced in particular detail because of their efficiency and their ability to deal with systems with Gaussian and/or non-Gaussian noise. The book also discusses information-filter and square-root-filtering algorithms, useful for state estimation in some real-time control system design problems. A number of case studies are included in the book to illustrate the application of various nonlinear filtering algorithms. Nonlinear Filtering is written for academic and industrial researchers, engineers and research students who are interested in nonlinear control systems analysis and design. The chief features of the book include: dedicated coverage of recently developed nonlinear, Jacobian-free, filtering algorithms; examples illustrating the use of nonlinear filtering algorithms in real-world applications; detailed derivation and complete algorithms for nonlinear filtering methods, which help readers to a fundamental understanding and easier coding of those algorithms; and MATLAB® codes associated with case-study applications, which can be downloaded from the Springer Extra Materials website.
| Author | : |
| Publisher | : |
| Total Pages | : 0 |
| Release | : 1998 |
| Genre | : |
| ISBN | : |
The final report contains the outline of the research that was done during the period 1995-98. The main objective was to develop effective numerical algorithms of optimal nonlinear filtering and prediction and (more generally), state and parameter estimation in partially observed stochastic dynamical systems. During the course of the project a number of fundamental results were obtained, such as: development of a Wiener type optimal nonlinear filter (complete solution of "the last Wiener problem"); development of the spectral based approach to nonlinear filtering, which have led to the spectral separating scheme (separation of parameters and observations in optimal nonlinear filter) and other effective numerical approximations for the optimal nonlinear filter that include projection filter and assumed density filters. The results have been applied to specific "difficult" problems in target tracking, particularly, to the angle only tracking in EO and IR search and track systems and track-before-detect of resolved or sub-resolved low SNR targets. Extensive simulation showed that the proposed approach allows us to obtain much better performance as compared to the conventional expended Kalman filter in a number of important practical situations.
| Author | : Peyman Setoodeh |
| Publisher | : John Wiley & Sons |
| Total Pages | : 308 |
| Release | : 2022-03-04 |
| Genre | : Technology & Engineering |
| ISBN | : 1119078156 |
NONLINEAR FILTERS Discover the utility of using deep learning and (deep) reinforcement learning in deriving filtering algorithms with this insightful and powerful new resource Nonlinear Filters: Theory and Applications delivers an insightful view on state and parameter estimation by merging ideas from control theory, statistical signal processing, and machine learning. Taking an algorithmic approach, the book covers both classic and machine learning-based filtering algorithms. Readers of Nonlinear Filters will greatly benefit from the wide spectrum of presented topics including stability, robustness, computability, and algorithmic sufficiency. Readers will also enjoy: Organization that allows the book to act as a stand-alone, self-contained reference A thorough exploration of the notion of observability, nonlinear observers, and the theory of optimal nonlinear filtering that bridges the gap between different science and engineering disciplines A profound account of Bayesian filters including Kalman filter and its variants as well as particle filter A rigorous derivation of the smooth variable structure filter as a predictor-corrector estimator formulated based on a stability theorem, used to confine the estimated states within a neighborhood of their true values A concise tutorial on deep learning and reinforcement learning A detailed presentation of the expectation maximization algorithm and its machine learning-based variants, used for joint state and parameter estimation Guidelines for constructing nonparametric Bayesian models from parametric ones Perfect for researchers, professors, and graduate students in engineering, computer science, applied mathematics, and artificial intelligence, Nonlinear Filters: Theory and Applications will also earn a place in the libraries of those studying or practicing in fields involving pandemic diseases, cybersecurity, information fusion, augmented reality, autonomous driving, urban traffic network, navigation and tracking, robotics, power systems, hybrid technologies, and finance.
| Author | : Chin-Pang Alex Fung |
| Publisher | : |
| Total Pages | : 240 |
| Release | : 1995 |
| Genre | : |
| ISBN | : |
| Author | : Sueo Sugimoto |
| Publisher | : Ohmsha, Ltd. |
| Total Pages | : 457 |
| Release | : 2020-12-10 |
| Genre | : Mathematics |
| ISBN | : 4274805026 |
This book covers a broad range of filter theories, algorithms, and numerical examples. The representative linear and nonlinear filters such as the Kalman filter, the steady-state Kalman filter, the H infinity filter, the extended Kalman filter, the Gaussian sum filter, the statistically linearized Kalman filter, the unscented Kalman filter, the Gaussian filter, the cubature Kalman filter are first visited. Then, the non-Gaussian filters such as the ensemble Kalman filter and the particle filters based on the sequential Bayesian filter and the sequential importance resampling are described, together with their recent advances. Moreover, the information matrix in the nonlinear filtering, the nonlinear smoother based on the Markov Chain Monte Carlo, the continuous-discrete filters, factorized filters, and nonlinear filters based on stochastic approximation method are detailed. 1 Review of the Kalman Filter and Related Filters 2 Information Matrix in Nonlinear Filtering 3 Extended Kalman Filter and Gaussian Sum Filter 4 Statistically Linearized Kalman Filter 5 The Unscented Kalman Filter 6 General Gaussian Filters and Applications 7 The Ensemble Kalman Filter 8 Particle Filter 9 Nonlinear Smoother with Markov Chain Monte Carlo 10 Continuous-Discrete Filters 11 Factorized Filters 12 Nonlinear Filters Based on Stochastic Approximation Method
| Author | : Zeev Schuss |
| Publisher | : Springer |
| Total Pages | : 262 |
| Release | : 2014-01-25 |
| Genre | : Mathematics |
| ISBN | : 9781489973818 |
This book offers an analytical rather than measure-theoretical approach to the derivation of the partial differential equations of nonlinear filtering theory. The basis for this approach is the discrete numerical scheme used in Monte-Carlo simulations of stochastic differential equations and Wiener's associated path integral representation of the transition probability density. Furthermore, it presents analytical methods for constructing asymptotic approximations to their solution and for synthesizing asymptotically optimal filters. It also offers a new approach to the phase tracking problem, based on optimizing the mean time to loss of lock. The book is based on lecture notes from a one-semester special topics course on stochastic processes and their applications that the author taught many times to graduate students of mathematics, applied mathematics, physics, chemistry, computer science, electrical engineering, and other disciplines. The book contains exercises and worked-out examples aimed at illustrating the methods of mathematical modeling and performance analysis of phase trackers.
| Author | : Nasir Uddin Ahmed |
| Publisher | : World Scientific |
| Total Pages | : 280 |
| Release | : 1998 |
| Genre | : Technology & Engineering |
| ISBN | : 9789810236090 |
"many new results, especially on nonlinear filtering problems and their numerical techniques, are included in book form for the first time it will serve as a useful reference book on the recent progress in this field. The book can be used for teaching graduate courses to students in mathematics, probability, statistics, and engineering. And finally, doctoral students and young researchers in the area of filtering theory and its applications can find inspiring ideas and techniques".Journal of Applied Mathematics and Stochastic Analysis, 2000
