Synopses & Reviews
Introduction to Optimal Estimation is an introductory but comprehensive treatment of the important topics of Kalman and Wiener filtering. In addition, least-squares, maximum-likelihood and maximum a posteriori (based on discrete-time measurements) estimation are developed, covering a broad range of techniques in a single textbook. Emphasis is placed on showing how these different approaches can be fitted together to form a systematic rationale for optimal estimation. The different matters to be addressed in actually computing estimates and characterizing the properties of estimates viewed as random variables are explained and underlined throughout. The text also incorporates study of nonlinear filtering, focusing on the extended Kalman filter and on a recently-developed nonlinear estimator based on a block-form version of the Levenberg-Marquardt algorithm. Introduction to Optimal Estimation is for use in a single course (or, with judicious pruning, a one-quarter course) on estimation by senior undergraduates or first-year graduate students. A number of the examples in this text were fashioned using MATLAB® and some of the homework problems require it. Students using this book will need to have completed a standard course on probability and random variables and at least one course in signals and systems including state-space theory for linear systems.
Synopsis
This book provides an introductory, yet comprehensive, treatment of both Wiener and Kalman filtering along with a development of least-squares estimation, maximum likelihood estimation, and maximum a posteriori estimation based on discrete-time measurements. Although this is a fairly broad range of estimation techniques, it is possible to cover all of them in some depth in a single textbook, which is what is attempted here. Emphasis is also placed on showing how these different approaches to estimation fit together to form a systematic development of optimal estimation. MATLAB is used in the development of a number of the book's examples and required for many of the homework problems.
Synopsis
The topics of control engineering and signal processing continue to flourish and develop. In common with general scientific investigation, new ideas, concepts and interpretations emerge quite spontaneously and these are then discussed, used, discarded or subsumed into the prevailing subject paradigm. Sometimes these innovative concepts coalesce into a new sub-discipline within the broad subject tapestry ofcontrol and signal processing. This preliminary batde between old and new usually takes place at conferences, through the Internet and in the journals of the discipline. After a litde more maturity has been acquiredhas been acquired by the new concepts then archival publication as ascientificorengineering monograph mayoccur. Anewconceptin control and signal processing is known to have arrived when sufficient material has developed for the topic to be taught as a specialised tutorial workshop or as a course to undergraduates, graduates or industrial engineers. The Advanced Textbooks in Control and Signal Processing Series is designed as a vehicle for the systematic presentation ofcourse material for both popular and innovative topics in the discipline. It is hoped that prospective authors will welcome the opportunity to publish a structured presentation of either existing subject areas or some of the newer emerging control and signal processing technologies.
Synopsis
A handy technical introduction to the latest theories and techniques of optimal estimation. It provides readers with extensive coverage of Wiener and Kalman filtering along with a development of least squares estimation, maximum likelihood and maximum a posteriori estimation based on discrete-time measurements. Much emphasis is placed on how they interrelate and fit together to form a systematic development of optimal estimation. Examples and exercises refer to MATLAB software.
Synopsis
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® and many of the problems discussed require the use of MATLAB®. 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.
Description
Includes bibliographical references (p. [371]-373) and index.
Table of Contents
Introduction.- Random Signals and Systems with Random Inputs.- Optimal Estimation.- The Wiener Filter.- Recursive Estimation and the Kalman Filter.- Further Development of the Kalman Filter.- Kalman Filter Applications.- Nonlinear Estimation.- Appendices: The State Representation; The z-transform; Stability of the Kalman Filter; The Steady-State Kalman Filter; Modeling Errors.