INTRODUCTION TO OPTIMAL ESTIMATION

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 MATLAB(r) and many of the problems discussed require the use of MATLAB(r). 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 maximum a posteriori estimation, based on discrete-time measurements. In the study of these estimation techniques there is a 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-Marquardt algorithm.