num_pages: 595
description: 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 parametersDetection of model changes including maneuver detection and time-varying PSD detectionComplex 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 (Prentice Hall PTR 1993 ISBN: 0-13-345711-7).
language: English
title: Fundamentals of Statistical Signal Processing: Detection Theory, Volume 2
publisher: Pearson