Statistical and Adaptive Signal Processing: Spectral Estimation, Signal Modeling, Adaptive Filtering and Array Processing | by Dimitris G. Manolakis, Vinay K. Ingle and Stephen M. Kogon | ISBN: 9781580536103. Discrete-Time Signal Processing. Optimum Linear Filters

November 26, 2009 by kutenk
Filed under: Engineering Books 

Statistical and Adaptive Signal Processing: Spectral Estimation, Signal Modeling, Adaptive Filtering and Array Processing

by Dimitris G. Manolakis, Vinay K. Ingle and Stephen M. Kogon
ISBN:9781580536103

Providing clear examples, problem sets, and computer experiments, this authoritative reference offers a unified, comprehensive and practical treatment of spectral estimation, signal modeling, adaptive filtering, and array processing.

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Statistical and Adaptive Signal Processing—Spectral Estimation, Signal Modeling, Adaptive Filtering, and Array Processing







Preface
APPROACHAND PREREQUISITES
ORGANIZATION OFTHE BOOK
THEORY AND PRACTICE

Chapter 1 – Introduction
OVERVIEW
1.1: RANDOM SIGNALS
1.2: SPECTRAL ESTIMATION
1.3: SIGNAL MODELING
1.4: ADAPTIVE FILTERING
1.5: ARRAY PROCESSING
1.6: ORGANIZATION OF THE BOOK




Chapter 2 – Fundamentals of Discrete-Time Signal Processing
OVERVIEW
2.1: DISCRETE-TIME SIGNALS
2.2: TRANSFORM-DOMAIN REPRESENTATION OF DETERMINISTIC SIGNALS
2.3: DISCRETE-TIME SYSTEMS
2.4: MINIMUM PHASE AND SYSTEM INVERTIBILITY
2.5: LATTICE FILTER REALIZATIONS
2.6: SUMMARY
PROBLEMS

Chapter 3 – Random Variables, Vectors, and Sequences
OVERVIEW
3.1: RANDOM VARIABLES
3.2: RANDOM VECTORS
3.3: DISCRETE-TIME STOCHASTIC PROCESSES
3.4: LINEAR SYSTEMS WITH STATIONARY RANDOM INPUTS
3.5: WHITENING AND INNOVATIONS REPRESENTATION
3.6: PRINCIPLES OF ESTIMATION THEORY
3.7: SUMMARY
PROBLEMS

Chapter 4 – Linear Signal Models
4.1: INTRODUCTION
4.2: ALL-POLE MODELS
4.3: ALL-ZERO MODELS
4.4: POLE-ZERO MODELS
4.5: MODELS WITH POLES ON THE UNIT CIRCLE
4.6: CEPSTRUM OF POLE-ZERO MODELS
4.7: SUMMARY
PROBLEMS




Chapter 5 – Nonparametric Power Spectrum Estimation
OVERVIEW
5.1: SPECTRAL ANALYSIS OF DETERMINISTIC SIGNALS
5.2: ESTIMATION OF THE AUTOCORRELATION OF STATIONARY RANDOM SIGNALS
5.3: ESTIMATION OF THE POWER SPECTRUM OF STATIONARY RANDOM SIGNALS
5.4: JOINT SIGNAL ANALYSIS
5.5: MULTITAPER POWER SPECTRUM ESTIMATION
5.6: SUMMARY
PROBLEMS

Chapter 6 – Optimum Linear Filters
OVERVIEW
6.1: OPTIMUM SIGNAL ESTIMATION
6.2: LINEAR MEAN SQUARE ERROR ESTIMATION
6.3: SOLUTION OF THE NORMAL EQUATIONS
6.4: OPTIMUM FINITE IMPULSE RESPONSE FILTERS
6.5: LINEAR PREDICTION
6.6: OPTIMUM INFINITE IMPULSE RESPONSE FILTERS
6.7: INVERSE FILTERING AND DECONVOLUTION
6.8: CHANNELEQUALIZATION IN DATATRANSMISSION SYSTEMS
6.9: MATCHED FILTERS AND EIGENFILTERS
6.10: SUMMARY
PROBLEMS

Chapter 7 – Algorithms and Structures for Optimum Linear Filters
OVERVIEW
7.1: FUNDAMENTALS OF ORDER-RECURSIVE ALGORITHMS
7.2: INTERPRETATIONS OF ALGORITHMIC QUANTITIES
7.3: ORDER-RECURSIVE ALGORITHMS FOR OPTIMUM FIR FILTERS
7.4: ALGORITHMS OF LEVINSON AND LEVINSON-DURBIN
7.5: LATTICE STRUCTURES FOR OPTIMUM FIR FILTERS AND PREDICTORS
7.6: ALGORITHM OF SCHÜR
7.7: TRIANGULARIZATION AND INVERSION OF TOEPLITZ MATRICES
7.8: KALMAN FILTER ALGORITHM
7.9: SUMMARY
PROBLEMS

Chapter 8 – Least-Squares Filtering and Prediction
8.1: THE PRINCIPLE OF LEAST SQUARES
8.2: LINEAR LEAST-SQUARES ERROR ESTIMATION
8.3: LEAST-SQUARES FIR FILTERS
8.4: LINEAR LEAST-SQUARES SIGNAL ESTIMATION
8.5: LS COMPUTATIONS USING THE NORMAL EQUATIONS
8.6: LS COMPUTATIONS USING ORTHOGONALIZATION TECHNIQUES
8.7: LS COMPUTATIONS USINGTHE SINGULAR VALUE DECOMPOSITION
8.8: SUMMARY
PROBLEMS

Chapter 9 – Signal Modeling and Parametric Spectral Estimation
OVERVIEW
9.1: THE MODELING PROCESS: THEORY AND PRACTICE
9.2: ESTIMATION OF ALL-POLE MODELS
9.3: ESTIMATION OF POLE-ZERO MODELS
9.4: APPLICATIONS
9.5: MINIMUM-VARIANCE SPECTRUM ESTIMATION
9.6: HARMONIC MODELS AND FREQUENCY ESTIMATION TECHNIQUES
9.7: SUMMARY
PROBLEMS

Chapter 10 – Adaptive Filters
OVERVIEW
10.1: TYPICALAPPLICATIONS OFADAPTIVE FILTERS
10.2: PRINCIPLES OFADAPTIVE FILTERS
10.3: METHOD OFSTEEPEST DESCENT
10.4: LEAST-MEAN-SQUAREADAPTIVE FILTERS
10.5: RECURSIVE LEAST-SQUARES ADAPTIVE FILTERS
10.6: RLSALGORITHMS FORARRAY PROCESSING
10.7: FAST RLS ALGORITHMS FOR FIR FILTERING
10.8: TRACKING PERFORMANCE OFADAPTIVEALGORITHMS
10.9: SUMMARY
PROBLEMS

Chapter 11 – Array Processing
OVERVIEW
11.1: ARRAY FUNDAMENTALS
11.2: CONVENTIONAL SPATIAL FILTERING: BEAMFORMING
11.3: OPTIMUM ARRAY PROCESSING
11.4: PERFORMANCE CONSIDERATIONS FOR OPTIMUM BEAMFORMERS
11.5: ADAPTIVE BEAMFORMING
11.6: OTHER ADAPTIVE ARRAY PROCESSING METHODS
11.7: ANGLE ESTIMATION
11.8: SPACE-TIME ADAPTIVE PROCESSING
11.9: SUMMARY
PROBLEMS

Chapter 12 – Further Topics
OVERVIEW
12.1: HIGHER-ORDER STATISTICS IN SIGNAL PROCESSING
12.2: BLIND DECONVOLUTION
12.3: UNSUPERVISED ADAPTIVE FILTERS—BLIND EQUALIZERS
12.4: FRACTIONALLY SPACED EQUALIZERS
12.5: FRACTIONAL POLE-ZERO SIGNAL MODELS
12.6: SELF-SIMILAR RANDOM SIGNAL MODELS
12.7: SUMMARY
PROBLEMS

Appendix A – Matrix Inversion Lemma
Appendix B – Gradients and Optimization in Complex Space
B.1: GRADIENT
B.2: LAGRANGE MULTIPLIERS

Appendix C – Matlab Functions
Appendix D – Useful Results from Matrix Algebra
D.1: COMPLEX-VALUED VECTOR SPACE
D.2: MATRICES
D.3: DETERMINANT OF A SQUARE MATRIX
D.4: UNITARY MATRICES
D.5: POSITIVE DEFINITE MATRICES

Appendix E – Minimum Phase Test for Polynomials
Bibliography

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