Fundamentals of Statistical Signal Processing

Steven Kay

ISBN 10: 0133457117 / ISBN 13: 9780133457117

Published by Pearson Education (US), 1993

About this Item

Intended for practicing engineers and scientists who design and analyze signal processing systems. This work offers a unified presentation of parameter estimation for those involved in the design and implementation of statistical signal processing algorithms. Num Pages: 625 pages. BIC Classification: PBW; TJK; UYS; UYT. Category: (U) Tertiary Education (US: College). Dimension: 243 x 187 x 24. Weight in Grams: 934. . 1993. 1st Edition. Hardcover. . . . . Books ship from the US and Ireland.

Seller Inventory # V9780133457117

Bibliographic Details

• Title: Fundamentals of Statistical Signal Processing
• Publisher: Pearson Education (US)
• Publication Date: 1993
• Language: English
• ISBN 10: 0133457117
• ISBN 13: 9780133457117
• Binding: Hardcover
• Condition: New

About this title

Synopsis

For practicing engineers and scientists who design and analyze signal processing systems, i.e., to extract information from noisy signals ― radar engineer, sonar engineer, geophysicist, oceanographer, biomedical engineer, communications engineer, economist, statistician, physicist, etc.

A unified presentation of parameter estimation for those involved in the design and implementation of statistical signal processing algorithms.

From the Back Cover

For those involved in the design and implementation of signal processing algorithms, this book strikes a balance between highly theoretical expositions and the more practical treatments, covering only those approaches necessary for obtaining an optimal estimator and analyzing its performance. Author Steven M. Kay discusses classical estimation followed by Bayesian estimation, and illustrates the theory with numerous pedagogical and real-world examples. Special features include over 230 problems designed to reinforce basic concepts and to derive additional results; summary chapter containing an overview of all principal methods and the rationale for choosing a particular one; unified treatment of Wiener and Kalman filtering; estimation approaches for complex data and parameters; and over 100 examples, including real-world applications to high resolution spectral analysis, system identification, digital filter design, adaptive noise cancelation, adaptive beamforming, tracking and localization, and more. Students as well as practicing engineers will find Fundamentals of Statistical Signal Processing an invaluable introduction to parameter estimation theory and a convenient reference for the design of successful parameter estimation algorithms.