Fundamentals of statistical signal processing: estimation theory. (English) Zbl 0803.62002

Kay, Steven M.

Prentice Hall Signal Processing Series. Englewood Cliffs, NJ: Prentice Hall International, Inc.. xii, 595 p. (1993).

The book presents an introduction to the theory of statistical estimation embedded in problems of statistical signal processing. The chapters 2 to 9 are devoted to the classical estimation theory, such as minimum variance unbiased estimation, Cramér-Rao lower bound, best linear unbiased estimators, maximum likelihood estimation, linear models, least squares, method of moments. The chapters 10 to 12 are concerned with Bayesian estimation including general Bayesian estimators and linear Bayesian estimators. Kalman filters are treated in chapter 13, and finally extensions for complex data and parameters are given.
Besides the presentation of the theoretical background for obtaining optimal estimators and analyzing their performance, the view to implementation of signal processing algorithms on a digital computer is supported. Numerous examples and applications to signal processing problems are helpful. The book is written on an intermediate level. The reader should have a basic knowledge of probability, statistics and digital signal processing. It is well readable and useful for readers interested in the basic estimation theory and its applications in signal processing.

Reviewer: H.Schellhaas (Darmstadt)

MSC:

62B10	Statistical aspects of information-theoretic topics
62F10	Point estimation
94A12	Signal theory (characterization, reconstruction, filtering, etc.)
94-01	Introductory exposition (textbooks, tutorial papers, etc.) pertaining to information and communication theory
62-01	Introductory exposition (textbooks, tutorial papers, etc.) pertaining to statistics

Keywords:

statistical signal processing; minimum variance unbiased estimation; Cramér-Rao lower bound; best linear unbiased estimators; maximum likelihood estimation; linear models; least squares; method of moments; Bayesian estimation; Kalman filters; algorithms; examples; applications