Statistical and Adaptive Signal Processing - Spectral Estimation Signal Modeling

807 Pages·2005·17.79 MB·English

by  Dimitris G. Manolakis

Statistical
and Adaptive
Signal Processing

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Statistical
and Adaptive
Signal Processing
Spectral Estimation, Signal Modeling, Adaptive
Filtering, and Array Processing
Dimitris G. Manolakis
Massachusetts Institute of Technology
Lincoln Laboratory
Vinay K. Ingle
Northeastern University
Stephen M. Kogon
Massachusetts Institute of Technology
Lincoln Laboratory
artechhouse.com
Library of Congress Cataloging-in-Publication Data
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British Library Cataloguing in Publication Data
A catalogue record for this book is available from the British Library.
This is a reissue of a McGraw-Hill book.
Cover design by Igor Valdman
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International Standard Book Number: 1-58053-610-7
10 9 8 7 6 5 4 3 2 1
To my beloved wife, Anna, and to the loving memory of my father, Gregory.
DGM
To my beloved wife, Usha, and adoring daughters, Natasha and Trupti.
VKI
To my wife and best friend, Lorna, and my children, Gabrielle and Matthias.
SMK
ABOUT THE AUTHORS
DIMITRISG.MANOLAKIS,anativeofGreece,receivedhiseducation(B.S.inphysics
andPh.D.inelectricalengineering)fromtheUniversityofAthens,Greece.Heiscurrently
amemberofthetechnicalstaffatMITLincolnLaboratory,inLexington,Massachusetts.
Previously,hewasaPrincipalMember,ResearchStaff,atRiversideResearchInstitute.Dr.
ManolakishastaughtattheUniversityofAthens,NortheasternUniversity,BostonCollege,
and Worcester Polytechnic Institute; and he is coauthor of the textbook Digital Signal
Processing: Principles, Algorithms, and Applications (Prentice-Hall, 1996, 3d ed.). His
research experience and interests include the areas of digital signal processing, adaptive
filtering, array processing, pattern recognition, and radar systems.
VINAY K. INGLE is Associate Professor of Electrical and Computer Engineering at
NortheasternUniversity.HereceivedhisPh.D.ineletricalandcomputerengineeringfrom
RensselaerPolytechnicInstitutein1981.Hehasbroadresearchexperienceandhastaught
courses on topics including signal and image processing, stochastic processes, and
estimationtheory.ProfessorIngleiscoauthorofthetextbooksDSPLaboratoryUsingthe
ADSP-2101 Microprocessor (Prentice-Hall, 1991) and DSP Using Matlab (PWS
Publishing Co., Boston, 1996).
STEPHENM.KOGONreceivedthePh.D.degreeinelectricalengineeringfromGeorgia
Institute of Technology. He is currently a member of the technical staff at MIT Lincoln
LaboratoryinLexington,Massachusetts.Previously,hehasbeenassociatedwithRaytheon
Co.,BostonCollege,andGeorgiaTechResearchInstitute.Hisresearchinterestsareinthe
areasofadaptiveprocessing,arraysignalprocessing,radar,andstatisticalsignalmodeling.
e56toc.qxd 3/16/05 11:56 AM Page x
CONTENTS
Preface xvii 2.3 Discrete-Time Systems 47
2.3.1 Analysis of Linear,
1 Introduction 1 Time-Invariant Systems / 2.3.2
Response to Periodic Inputs / 2.3.3
1.1 Random Signals 1 Correlation Analysis and Spectral
Density
1.2 Spectral Estimation 8
2.4 Minimum-Phase and
1.3 Signal Modeling 11
System Invertibility 54
1.3.1Rational orPole-Zero
2.4.1 System Invertibility and
Models / 1.3.2 Fractional
Minimum-Phase Systems /
Pole-Zero Models and
2.4.2 All-Pass Systems / 2.4.3
Fractal Models
Minimum-Phase and All-Pass
1.4 Adaptive Filtering 16 Decomposition / 2.4.4 Spectral
1.4.1 Applications of Adaptive Factorization
Filters / 1.4.2 Features of 2.5 Lattice Filter Realizations 64
Adaptive Filters
2.5.1 All-Zero Lattice Structures /
1.5 Array Processing 25 2.5.2 All-Pole Lattice Structures
1.5.1 Spatial Filtering or 2.6 Summary 70
Beamforming/1.5.2 Adaptive
Problems 70
Interference Mitigation in
Radar Systems/1.5.3 Adaptive
3 Random Variables,Vectors,
Sidelobe Canceler
and Sequences 75
1.6 Organization of the Book 29
3.1 Random Variables 75
2 Fundamentals of Discrete-
Time Signal Processing 33 3.1.1 Distribution and Density
Functions / 3.1.2 Statistical
Averages / 3.1.3 Some Useful
2.1 Discrete-Time Signals 33
Random Variables
2.1.1 Continuous-Time,Discrete-
3.2 Random Vectors 83
Time,and Digital Signals /
2.1.2 Mathematical Description of 3.2.1 Definitions and Second-Order
Signals / 2.1.3 Real-World Signals Moments / 3.2.2 Linear
Transformations of Random
2.2 Transform-Domain
Vectors / 3.2.3 Normal Random
Representation of
Vectors / 3.2.4 Sums of Independent
Deterministic Signals 37
Random Variables
2.2.1 Fourier Transforms and
3.3 Discrete-Time Stochastic
Fourier Series / 2.2.2 Sampling
Processes 97
of Continuous-Time Signals /
2.2.3 The Discrete Fourier 3.3.1 Description Using
Transform / 2.2.4 The Probability Functions / 3.3.2
z-Transform / 2.2.5 Representation Second-Order Statistical
of Narrowband Signals Description / 3.3.3 Stationarity /
x