Introduction to Spectral Analysis - Book Review,
by Petre Stoica

Book News, Inc. A textbook for an introductory senior or first-year graduate course for students familiar with linear algebra, discrete-time linear systems, and introductory discrete-time stochastic processes or time series. A basic understanding of estimation theory is also helpful. Uses specific functions of MATLAB, which are available along with other support material from the World Wide Web. -- Copyright © 1999 Book News, Inc., Portland, OR All rights reserved
Book Description This book presents an introduction to spectral analysis that is designed for either course use or self-study. Clear and concise in approach, it develops a firm understanding of tools and techniques as well as a solid background for performing research. Topics covered include nonparametric spectrum analysis (both periodogram-based approaches and filter- bank approaches), parametric spectral analysis using rational spectral models (AR, MA, and ARMA models), parametric method for line spectra, and spatial (array) signal processing. Analytical and Matlab-based computer exercises are included to develop both analytical skills and hands-on experience.
The publisher, Prentice-Hall Engineering/Science/Mathematics This text presents an introduction to spectral analysis that is designed for either course use or self-study. Clear and concise in approach, it covers both classical and modern approaches of spectral analysis. Topics covered include nonparametric spectrum analysis (both periodogram- based approaches and filter-bank approaches), parametric spectral analysis using rational spectral models (AR, MA, and ARMA models), parametric method for line spectra, and spatial (array) signal processing. Analytical and Matlab-based computer exercises are included to develop both analytical skills and hands-on experience.
From the Back Cover This book presents an introduction to spectral analysis that is designed for either course use or self-study. Clear and concise in approach, it develops a firm understanding of tools and techniques as well as a solid background for performing research. Topics covered include nonparametric spectrum analysis (both periodogram-based approaches and filter- bank approaches), parametric spectral analysis using rational spectral models (AR, MA, and ARMA models), parametric method for line spectra, and spatial (array) signal processing. Analytical and Matlab-based computer exercises are included to develop both analytical skills and hands-on experience.
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