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Zbl 0853.62076
Bar-Shalom, Yaakov; Li, Xiao-Rong
Estimation and tracking. Principles, techniques, and software. Incl. 1 disk. (English)
[B] Norwood, MA: Artech House. xxii, 511 p. \$ 79.00 (1993). ISBN 0-89006-643-4

This text is a set of lecture notes presenting the material of a standard course in Control and Communication Systems. It starts with short reviews of linear algebra, linear systems, probability theory and statistical estimation theory. The third chapter on linear estimation in static systems presents the minimum mean square error linear estimation of random vectors, especially polynomial fitting. Then linear dynamical systems with random inputs and state estimation in discrete-time linear dynamical systems are dealt with. This is applied to estimation of kinematic models that are obtained by discretizing continuous-time state representations. The methods for discrete time are looked at further from the computational aspect and with respect to extensions. Chapter nine deals with continuous-time linear state estimation. Finally nonlinear systems are considered.\par A disk is included containing a menu-driven interactive design- and evaluation tool for Kalman filters and an adaptive interacting multiple model estimator. The software is programmed in MATLAB that is also necessary for running it. Each chapter starts with an outline and a summary of objectives and closes with a subsection `notes and problems'.\par The text is really a set of lectures. So the reader should not expect lengthy discussions and explanations but a good basis for one who knows the topic already or who is willing to consult other texts as well.
[R.Schlittgen (Hamburg)]

MSC 2000:: *62N99 Survival analysis and censored data
93-01 Textbooks (systems and control)
62P99 Appl. of statistics
62-04 Machine computation, programs (statistics)
62-01 Textbooks (statistics)
93-04 Machine computation, programs (systems and control)

Keywords: minimum mean square error linear estimation; polynomial fitting; state estimation; estimation of kinematic models; nonlinear systems; Kalman filters; MATLAB

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