Overview

Spectrum analysis can be considered as a topic in statistics as well as a topic in digital signal processing (DSP). This book takes a middle course by emphasizing the time series models and their impact on spectrum analysis.The text begins with elements of probability theory and goes on to introduce the theory of stationary stochastic processes. The depth of coverage is extensive. Many topics of concern to spectral characterization of Gaussian and non-Gaussian time series, scalar and vector time series are covered. A section is devoted to the emerging areas of non-stationary and cyclostationary time series.The book is organized more as a textbook than a reference book. Each chapter includes many examples to illustrate the concepts described. Several exercises are included at the end of each chapter. The level is appropriate for graduate and research students.

Full Product Details

Author:   Prabhakar S. Naidu (Indian Institute of Science, Bangalore, India)
Publisher:   Taylor & Francis Inc
Imprint:   CRC Press Inc
Dimensions:   Width: 17.80cm , Height: 2.80cm , Length: 25.40cm
Weight:   0.907kg
ISBN:  

9780849324642

ISBN 10:   0849324645
Pages:   416
Publication Date:   25 October 1995
Audience:   College/higher education ,  Professional and scholarly ,  Postgraduate, Research & Scholarly ,  Professional & Vocational
Format:   Hardback
Publisher's Status:   Out of Print
Availability:   Out of stock  

Table of Contents

Stochastic Characterization of Time Series. Time Series as a Stochastic Process. A Review of Stochastic Process. Stationary Stochastic Process: Second Order. Spectral Representation. Stationary Stochastic Process: Third Order. Vector Stochastic Process. Nonstationary Process. Exercises. Mathematical Models of Time Series. Time Series Models. Filter Model. Discrete Fourier Transform (DFT). Parametric Models: MA/AR. Parametic Models: ARMA. Parametic Bispectral Model. Deterministic Chaos. Exercises. Spectrum Estimation: Low Resolution Methods. An Overview. Covariance Function. Estimation of Spectrum and Cross-Spectrum. Estimation of Coherence. Spectrum of Window Function. Estimation of Bicovariance and Bispectrum. Estimation of Time Varying Spectrum. Exercises. Spectrum Estimation: High Resolution Methods. An Overview. Maximum Likelihood (ML) Spectrum. Maximum Entropy (ME) Spectrum. Parametric Spectrum. Subspace Methods. Nonlinear Transformation. Extrapolation of Band Limited Time Series. Exercises. Spectrum Estimation : Data Adaptive Approach. Data Adaptive Approach. Prewhitening. Burg Spectrum. Data Matrix and Singular Value Decomposition. Adaptive Subspace. Exercises.

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Naidu\, Prabhakar S.

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