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A foundational textbook on estimation theory for statistical signal processing, this volume offers a unified presentation of parameter estimation from noisy signals. It covers classical and Bayesian estimation methods, including minimum variance unbiased estimation, Cramér–Rao bounds, maximum likelihood, least squares, method of moments, Bayesian estimators, and Kalman filters, with numerous examples and real‑world applications to radar, communications, biomedical, and other signal‑processing problems.

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245: $a: Fundamentals of Statistical Signal Processing: Estimation Theory
245: $b:
082: $a:
100: $a: Steven M. Kay
650: $a: Signal Processing
250: $a:
440: $v:
250: $b: Prentice Hall PTR
250: $c: 1993
020: $a: 9780133457117
300: $a: 608