Filtering for Stochastic Processes with Applications to Guidance
Filtering for Stochastic Processes with Applications to Guidance

Author: Richard S. Bucy

Publisher: American Mathematical Soc.

Published: 2005

Total Pages: 240

ISBN-13: 9780821837825

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This second edition preserves the original text of 1968, with clarification and added references. From the Preface to the Second Edition: ``Since the First Edition of this book, numerous important results have appeared--in particular stochastic integrals with respect to martingales, random fields, Riccati equation theory and realization of nonlinear filters, to name a few. In Appendix D, an attempt is made to provide some of the references that the authors have found useful and tocomment on the relation of the cited references to the field ... [W]e hope that this new edition will have the effect of hastening the day when the nonlinear filter will enjoy the same popularity in applications as the linear filter does now.''

Discrete Stochastic Processes and Optimal Filtering
Discrete Stochastic Processes and Optimal Filtering

Author: Jean-Claude Bertein

Publisher: John Wiley & Sons

Published: 2012-12-27

Total Pages: 196

ISBN-13: 1118600533

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Optimal filtering applied to stationary and non-stationary signals provides the most efficient means of dealing with problems arising from the extraction of noise signals. Moreover, it is a fundamental feature in a range of applications, such as in navigation in aerospace and aeronautics, filter processing in the telecommunications industry, etc. This book provides a comprehensive overview of this area, discussing random and Gaussian vectors, outlining the results necessary for the creation of Wiener and adaptive filters used for stationary signals, as well as examining Kalman filters which are used in relation to non-stationary signals. Exercises with solutions feature in each chapter to demonstrate the practical application of these ideas using MATLAB.

Linear Least-squares Estimation
Linear Least-squares Estimation

Author: Thomas Kailath

Publisher: Hutchinson Ross Publishing Company

Published: 1977

Total Pages: 344

ISBN-13:

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A survey of the field; Mathematical foundations of least-squares prediction theory; Wiener-hopf equations and optimum filters; State-space models and recursive filters.

Linear Stochastic Systems
Linear Stochastic Systems

Author: Peter E. Caines

Publisher: SIAM

Published: 2018-06-12

Total Pages: 892

ISBN-13: 1611974704

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Linear Stochastic Systems, originally published in 1988, is today as comprehensive a reference to the theory of linear discrete-time-parameter systems as ever. Its most outstanding feature is the unified presentation, including both input-output and state space representations of stochastic linear systems, together with their interrelationships. The author first covers the foundations of linear stochastic systems and then continues through to more sophisticated topics including the fundamentals of stochastic processes and the construction of stochastic systems; an integrated exposition of the theories of prediction, realization (modeling), parameter estimation, and control; and a presentation of stochastic adaptive control theory. Written in a clear, concise manner and accessible to graduate students, researchers, and teachers, this classic volume also includes background material to make it self-contained and has complete proofs for all the principal results of the book. Furthermore, this edition includes many corrections of errata collected over the years.

Bayesian Filtering and Smoothing
Bayesian Filtering and Smoothing

Author: Simo Särkkä

Publisher: Cambridge University Press

Published: 2013-09-05

Total Pages: 255

ISBN-13: 110703065X

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A unified Bayesian treatment of the state-of-the-art filtering, smoothing, and parameter estimation algorithms for non-linear state space models.

Nonlinear Filtering and Smoothing
Nonlinear Filtering and Smoothing

Author: Venkatarama Krishnan

Publisher: Courier Corporation

Published: 2013-10-17

Total Pages: 353

ISBN-13: 0486781836

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Most useful for graduate students in engineering and finance who have a basic knowledge of probability theory, this volume is designed to give a concise understanding of martingales, stochastic integrals, and estimation. It emphasizes applications. Many theorems feature heuristic proofs; others include rigorous proofs to reinforce physical understanding. Numerous end-of-chapter problems enhance the book's practical value. After introducing the basic measure-theoretic concepts of probability and stochastic processes, the text examines martingales, square integrable martingales, and stopping times. Considerations of white noise and white-noise integrals are followed by examinations of stochastic integrals and stochastic differential equations, as well as the associated Ito calculus and its extensions. After defining the Stratonovich integral, the text derives the correction terms needed for computational purposes to convert the Ito stochastic differential equation to the Stratonovich form. Additional chapters contain the derivation of the optimal nonlinear filtering representation, discuss how the Kalman filter stands as a special case of the general nonlinear filtering representation, apply the nonlinear filtering representations to a class of fault-detection problems, and discuss several optimal smoothing representations.