Approximate Kalman Filtering
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Approximate Kalman Filtering
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Author : Guanrong Chen
language : en
Publisher: World Scientific
Release Date : 1993-08-30
Approximate Kalman Filtering written by Guanrong Chen and has been published by World Scientific this book supported file pdf, txt, epub, kindle and other format this book has been release on 1993-08-30 with Technology & Engineering categories.
Kalman filtering algorithm gives optimal (linear, unbiased and minimum error-variance) estimates of the unknown state vectors of a linear dynamic-observation system, under the regular conditions such as perfect data information; complete noise statistics; exact linear modeling; ideal well-conditioned matrices in computation and strictly centralized filtering.In practice, however, one or more of the aforementioned conditions may not be satisfied, so that the standard Kalman filtering algorithm cannot be directly used, and hence “approximate Kalman filtering” becomes necessary. In the last decade, a great deal of attention has been focused on modifying and/or extending the standard Kalman filtering technique to handle such irregular cases. It has been realized that approximate Kalman filtering is even more important and useful in applications.This book is a collection of several tutorial and survey articles summarizing recent contributions to the field, along the line of approximate Kalman filtering with emphasis on both its theoretical and practical aspects.
Approximate Kalman Filtering
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Author : Guanrong Chen
language : en
Publisher: World Scientific
Release Date : 1993
Approximate Kalman Filtering written by Guanrong Chen and has been published by World Scientific this book supported file pdf, txt, epub, kindle and other format this book has been release on 1993 with Computers categories.
Kalman filtering algorithm gives optimal (linear, unbiased and minimum error-variance) estimates of the unknown state vectors of a linear dynamic-observation system, under the regular conditions such as perfect data information; complete noise statistics; exact linear modeling; ideal well-conditioned matrices in computation and strictly centralized filtering.In practice, however, one or more of the aforementioned conditions may not be satisfied, so that the standard Kalman filtering algorithm cannot be directly used, and hence ?approximate Kalman filtering? becomes necessary. In the last decade, a great deal of attention has been focused on modifying and/or extending the standard Kalman filtering technique to handle such irregular cases. It has been realized that approximate Kalman filtering is even more important and useful in applications.This book is a collection of several tutorial and survey articles summarizing recent contributions to the field, along the line of approximate Kalman filtering with emphasis on both its theoretical and practical aspects.
Kalman Filtering And Neural Networks
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Author : Simon Haykin
language : en
Publisher: John Wiley & Sons
Release Date : 2004-03-24
Kalman Filtering And Neural Networks written by Simon Haykin and has been published by John Wiley & Sons this book supported file pdf, txt, epub, kindle and other format this book has been release on 2004-03-24 with Technology & Engineering categories.
State-of-the-art coverage of Kalman filter methods for the design of neural networks This self-contained book consists of seven chapters by expert contributors that discuss Kalman filtering as applied to the training and use of neural networks. Although the traditional approach to the subject is almost always linear, this book recognizes and deals with the fact that real problems are most often nonlinear. The first chapter offers an introductory treatment of Kalman filters with an emphasis on basic Kalman filter theory, Rauch-Tung-Striebel smoother, and the extended Kalman filter. Other chapters cover: An algorithm for the training of feedforward and recurrent multilayered perceptrons, based on the decoupled extended Kalman filter (DEKF) Applications of the DEKF learning algorithm to the study of image sequences and the dynamic reconstruction of chaotic processes The dual estimation problem Stochastic nonlinear dynamics: the expectation-maximization (EM) algorithm and the extended Kalman smoothing (EKS) algorithm The unscented Kalman filter Each chapter, with the exception of the introduction, includes illustrative applications of the learning algorithms described here, some of which involve the use of simulated and real-life data. Kalman Filtering and Neural Networks serves as an expert resource for researchers in neural networks and nonlinear dynamical systems.
Two Cases Of Approximation For Kalman Filtering
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Author : Michael Antonio Rodriguez
language : en
Publisher:
Release Date : 1971
Two Cases Of Approximation For Kalman Filtering written by Michael Antonio Rodriguez and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 1971 with categories.
Approximate Linear Regulator And Kalman Filter
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Author :
language : en
Publisher:
Release Date : 1980
Approximate Linear Regulator And Kalman Filter written by and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 1980 with categories.
Practical dynamic systems constantly face unpredictable fluctuations and disturbances for which Kalman filter has been shown to be effective in estimating the states from the outputs corrupted by white noises. This is the Kalman filtering problem. On the other hand, the Linear regulator problem, which is the mathematical dual of the Kalman filtering problem, plays an important role in modern optimal control theory. Both problems can be formulated as quadratic synthesis problems. A geometric-series approach is used to approximate the exponentials of Hamiltonian matrices for the quadratic synthesis problems. The approximants of the discretized transition matrices are then used to construct piecewise-constant gains and piecewise time-varying gains for approximating time-varying optimal gains and time-varying Kalman gains. Simple and fast algorithms are developed and can be easily implemented on a low cost minicomputer or microprocessor. The proposed methods have been successfully applied to the analysis of practical control systems.
Kalman Filtering Under Information Theoretic Criteria
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Author : Badong Chen
language : en
Publisher: Springer Nature
Release Date : 2023-09-19
Kalman Filtering Under Information Theoretic Criteria written by Badong Chen and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-09-19 with Technology & Engineering categories.
This book provides several efficient Kalman filters (linear or nonlinear) under information theoretic criteria. They achieve excellent performance in complicated non-Gaussian noises with low computation complexity and have great practical application potential. The book combines all these perspectives and results in a single resource for students and practitioners in relevant application fields. Each chapter starts with a brief review of fundamentals, presents the material focused on the most important properties and evaluates comparatively the models discussing free parameters and their effect on the results. Proofs are provided at the end of each chapter. The book is geared to senior undergraduates with a basic understanding of linear algebra, signal processing and statistics, as well as graduate students or practitioners with experience in Kalman filtering.
Kalman Filtering
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Author : Charles K. Chui
language : en
Publisher: Springer Science & Business Media
Release Date : 2013-06-29
Kalman Filtering written by Charles K. Chui and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2013-06-29 with Science categories.
In addition to making a number of minor corrections and updat ing the references, we have expanded the section on "real-time system identification" in Chapter 10 of the first edition into two sections and combined it with Chapter 8. In its place, a very brief introduction to wavelet analysis is included in Chapter 10. Although the pyramid algorithms for wavelet decompositions and reconstructions are quite different from the Kalman filtering al gorithms, they can also be applied to time-domain filtering, and it is hoped that splines and wavelets can be incorporated with Kalman filtering in the near future. College Station and Houston Charles K. Chui September 1990 Guanrong Chen Preface to the First Edition Kalman filtering is an optimal state estimation process applied to a dynamic system that involves random perturbations. More precisely, the Kalman filter gives a linear, unbiased, and min imum error variance recursive algorithm to optimally estimate the unknown state of a dynamic system from noisy data taken at discrete real-time. It has been widely used in many areas of industrial and government applications such as video and laser tracking systems, satellite navigation, ballistic missile trajectory estimation, radar, and fire control. With the recent development of high-speed computers, the Kalman filter has become more use ful even for very complicated real-time applications.
Kalman Filtering In Non Gaussian Environment Using Efficient Score Function Approximation
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Author : Wen-Rong Wu
language : en
Publisher:
Release Date : 1989
Kalman Filtering In Non Gaussian Environment Using Efficient Score Function Approximation written by Wen-Rong Wu and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 1989 with categories.
Kalman Filtering Theory
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Author : A. V. Balakrishnan
language : en
Publisher:
Release Date : 1987
Kalman Filtering Theory written by A. V. Balakrishnan and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 1987 with Control theory categories.
Effect Of Inexact Mechanization In Real Time Kalman Filtering
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Author : Guanrong Chen
language : en
Publisher:
Release Date : 1992
Effect Of Inexact Mechanization In Real Time Kalman Filtering written by Guanrong Chen and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 1992 with Approximation theory categories.
In this paper, a computational aspect of real-time estimation algorithm implementation is considered, in which the estimation algorithm to be used is defined having the standard optimal Kalman filtering structure but with the actual inverse matrix within the Kalman gain being replaced by an expedient approximation at each time instant. In real-time applications, most Kalman filtering schemes are approximate to a degree as a consequence of computational errors in calculating matrix inversion due to numerical roundoff. Convergence properties and error estimates of such approximate mechanizations of the Kalman filtering algorithm are obtained in this paper to provide a theoretical basis for gauging the utility of using computational approximations of the Kalman gain matrix at each time instant viz use of exponentially convergent sequences for comparison. A new exponentially convergent scheme is also suggested for approximating the inverse matrix within the Kalman gain. Conditions are offered when on-line approximate matrix inversion can be eliminated as the scapegoat cause of Kalman filter divergence in real-time mechanization leaving only model mismatch as the likely culprit when divergence occurs.