Fundamentals of Statistical Signal Processing : Detection Theory, Volume 2

Steven Kay

ISBN 10: 013504135X / ISBN 13: 9780135041352

Published by Pearson Education Jan 1998, 1998

About this Item

Neuware - This book focuses on techniques for obtaining optimal detection algorithms for implementation on digital computers.KEY TOPICS:The book explains statistical and signal processing in the context of numerous practical examples, focusing on current detection applications - especially problems in speech and communications. The book makes extensive use of MATLAB, and program listings are included wherever appropriate. Topics covered include: probability density functions and properties; statistical decision theory for both deterministic and random signals; signals with unknown parameters; white and colored Gaussian noise; non-Gaussian noise; detectors; model change detection; complex extensions; vector generalization and array processing. This is the perfect companion to Fundamentals of Statistical Signal Processing, Vol. 1: Estimation Theory.MARKET:For practicing electrical engineers building detectors for real-world applications. Also for electronics students and researchers.

Seller Inventory # 9780135041352

Bibliographic Details

• Title: Fundamentals of Statistical Signal Processing : Detection Theory, Volume 2
• Publisher: Pearson Education Jan 1998
• Publication Date: 1998
• Language: English
• ISBN 10: 013504135X
• ISBN 13: 9780135041352
• Binding: Buch
• Condition: Neu

About this title

Synopsis

The most comprehensive overview of signal detection available.

This is a thorough, up-to-date introduction to optimizing detection algorithms for implementation on digital computers. It focuses extensively on real-world signal processing applications, including state-of-the-art speech and communications technology as well as traditional sonar/radar systems.

Start with a quick review of the fundamental issues associated with mathematical detection, as well as the most important probability density functions and their properties. Next, review Gaussian, Chi-Squared, F, Rayleigh, and Rician PDFs, quadratic forms of Gaussian random variables, asymptotic Gaussian PDFs, and Monte Carlo Performance Evaluations.

Three chapters introduce the basics of detection based on simple hypothesis testing, including the Neyman-Pearson Theorem, handling irrelevant data, Bayes Risk, multiple hypothesis testing, and both deterministic and random signals.

The author then presents exceptionally detailed coverage of composite hypothesis testing to accommodate unknown signal and noise parameters. These chapters will be especially useful for those building detectors that must work with real, physical data. Other topics covered include:

• Detection in nonGaussian noise, including nonGaussian noise characteristics, known deterministic signals, and deterministic signals with unknown parameters
• Detection of model changes, including maneuver detection and time-varying PSD detection
• Complex extensions, vector generalization, and array processing

The book makes extensive use of MATLAB, and program listings are included wherever appropriate. Designed for practicing electrical engineers, researchers, and advanced students, it is an ideal complement to Steven M. Kay's Fundamentals of Statistical Signal Processing, Vol. 1: Estimation Theory (ISBN: 0-13-345711-7).

About the Author

STEVEN M. KAY is Professor of Electrical Engineering at the University of Rhode Island and a leading expert in signal processing.