Fundamentals of statistical signal processing: estimation theory | Guide books

Fundamentals of statistical signal processing: estimation theoryMarch 1993

Author:
Steven M. Kay
Univ. of Rhode Island, Kingston

Publisher:

Prentice-Hall, Inc.
Division of Simon and Schuster One Lake Street Upper Saddle River, NJ
United States

ISBN:978-0-13-345711-7

Published:01 March 1993

Pages:

595

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Stephen M Kay
The University of Rhode Island
- Publication Years1987 - 2022
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Reviewer: Michael L. Hilton

Estimation theory is concerned with extracting information, in the form of various parameters, from signals. Such parameter estimation plays an important role in many of today's digital signal processing applications. Kay's book is an excellent introduction to estimation theory. The book is split into two parts. The first part deals with classical estimation and has chapters on minimum variance unbiased estimation, the Cramer-Rao lower bound, linear models, linear unbiased estimators, maximum likelihood estimation, least squares estimation, and the method of moments. The second part deals with Bayesian estimation and has chapters on general and linear Bayesian estimators (including a discussion of Wiener filtering) and Kalman filtering. A summary chapter doubles as a guide for choosing which estimator to use in a particular situation. This book is aimed at both graduate students and practicing engineers. Readers are assumed to have a basic knowledge of signal processing, probability and random processes, and linear algebra. In the book's preface, Kay states his text is “an attempt to strike a balance” between theory and practice. He has achieved this goal. The mathematics behind parameter estimation is presented in a well-motivated, accessible manner. Kay takes the time to explain the significance of the problems being tackled and of the mathematical solutions. Theorems and results are stated and put to use in each chapter's main text, with the proofs given in appendices that follow each chapter. Several running examples, such as estimating a DC level in additive white Gaussian noise, are used throughout the book to illustrate the differences between estimation techniques. Many of the chapters have a section on signal processing applications, including adaptive beamforming, blind deconvolution, frequency estimation, and vehicle tracking. Each chapter also includes a set of exercises and a list of bibliographic references.

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