PLEASE PROVIDE ??? Num Pages: 672 pages, Illustrations. BIC Classification: TJK; UYS. Category: (U) Tertiary Education (US: College). Dimension: 235 x 185 x 34. Weight in Grams: 1026. . 1998. 1st Edition. Hardcover. . . . .

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D�tails bibliographiques

Titre: Fundamentals of Statistical Signal Processing
�diteur: Pearson Education (US)
Date de parution: 1998
Langue: anglais
ISBN � 10 chiffres: 013504135X
ISBN � 13 chiffres: 9780135041352
Reliure: Couverture rigide
�dition: Edition originale
�tat de l'article: New

A propos de ce titre

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).

� propos de l?auteur

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

Les informations fournies dans la section � A propos du livre � peuvent faire r�f�rence � une autre �dition de ce titre.

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