โ Scribed by E. W. Kamen PhD, J. K. Su PhD (auth.)
No coin nor oath required. For personal study only.
This book, developed from a set of lecture notes by Professor Kamen, and since expanded and refined by both authors, is an introductory yet comprehensive study of its field. It contains examples that use MATLABยฎ and many of the problems discussed require the use of MATLABยฎ. The primary objective is to provide students with an extensive coverage of Wiener and Kalman filtering along with the development of least squares estimation, maximum likelihood estimation and a posteriori estimation, based on discrete-time measurements. In the study of these estimation techniques there is strong emphasis on how they interrelate and fit together to form a systematic development of optimal estimation. Also included in the text is a chapter on nonlinear filtering, focusing on the extended Kalman filter and a recently-developed nonlinear estimator based on a block-form version of the Levenberg-Marquadt Algorithm.
Front Matter....Pages I-XIII
Introduction....Pages 1-26
Random Signals and Systems with Random Inputs....Pages 27-68
Optimal Estimation....Pages 69-100
The Wiener Filter....Pages 101-147
Recursive Estimation and the Kalman Filter....Pages 149-189
Further Development of the Kalman Filter....Pages 191-223
Kalman Filter Applications....Pages 225-267
Nonlinear Estimation....Pages 269-311
Back Matter....Pages 313-380
Control; Signal, Image and Speech Processing; Probability Theory and Stochastic Processes; Systems Theory, Control; Statistics for Engineering, Physics, Computer Science, Chemistry and Earth Sciences; Control, Robotics, Mechatronics
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