Approximate Kalman Filtering

By: Guanrong Chen (Editor)

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Hardcover | 1 December 1992

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240 Pages

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22.86 x 16.51 x 1.91

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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 modelling; ideal will-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. This book is a collection of several survey articles summarizing recent contributions to the field, along the line of approximate Kalman filtering with emphasis on its practical aspects.

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Preface
Extended Kalman Filtering for Nonlinear Systems
Extended Kalman Filters 1: Continuous and Discrete Linearizations	p. 3
Extended Kalman Filters 2: Standard, Modified and Ideal	p. 9
Extended Kalman Filters 3: A Mathematical Analysis of Bias	p. 15
Initialization of Kalman Filtering
Fisher Initialization in the Presence of Ill-Conditioned Measurements	p. 23
Initializing the Kalman Filter with Incompletely Specified Initial Conditions	p. 39
Adaptive Kalman Filtering in Irregular Environments
Robust Adaptive Kalman Filtering	p. 65
On-line Estimation of Signal and Noise Parameters and the Adaptive Kalman Filtering	p. 87
Suboptimal Kalman Filtering for Linear Systems with Non-Gaussian Noise	p. 113
Set-valued and Distributed Kalman Filtering
Set-valued Kalman Filtering	p. 139
Distributed Filtering Using Set Models for Systems with Non-Gaussian Noise	p. 161
Stability Analysis and Numerical Approximation of Kalman Filtering
Robust Stability Analysis of Kalman Filter under Parametric and Noise Uncertainties	p. 179
Numerical Approximations and Other Structural Issues in Practical Implementations of Kalman Filtering	p. 193
Further Reading	p. 221
Notation	p. 223
Subject Index	p. 225
Table of Contents provided by Blackwell. All Rights Reserved.

Approximate Kalman Filtering

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