Linear and Nonlinear Filtering for Scientists and Engineers

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The book combines both rigor and intuition to derive the most from classical results of linear and nonlinear filtering and beyond. Many fundamental results recently discovered by the author are included. Furthermore, many results that have appeared in recent years in the literature are also presented. The most interesting feature of the book is that all the derivations of the linear filter equations given in Chapters 3-11, beginning from the classical Kalman filter presented in Chapters 3 and 5, are based on one basic principle which is fully rigorous but also very intuitive and easily understandable, The second most interesting feature is that the book provides a rigorous theoretical basis for the numerical solution of nonlinear filter equations illustrated by multidimensional examples. The book also provides a strong foundation for theoretical understanding of the subject based on the theory of stochastic differential equations.
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Product Details

  • ISBN-13: 9789810236090
  • Publisher: World Scientific Publishing Company, Incorporated
  • Publication date: 3/28/1999
  • Series: Applied Mathematics Series
  • Pages: 272
  • Product dimensions: 6.34 (w) x 8.82 (h) x 0.79 (d)

Table of Contents

Ch. 1 Introduction to Stochastic Processes
Ch. 2 Stochastic Differential Equations
Ch. 3 Kalman Filtering for Linear Systems Driven by Wiener Process-I
Ch. 4 Kalman Filtering for Linear Systems Driven by Wiener Process-II
Ch. 5 Discrete Kalman Filtering
Ch. 6 Linear Filtering with Correlated Noise-I
Ch. 7 Linear Filtering with Correlated Noise-II
Ch. 8 Linear Filtering with Correlated Noise-III
Ch. 9 Linear Filtering of Jump Processes
Ch. 10 Linear Filtering with Constraints
Ch. 11 Filtering for Linear Systems Driven by Second Order Random Processes
Ch. 12 Extended Kalman Filtering I, II and III
Ch. 13 Nonlinear Filtering
Ch. 14 Numerical Techniques for Nonlinear Filtering
Ch. 15 Partially Observed Control
Ch. 16 System Identification
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