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E-raamat: Adaptive Signal Processing - Next Generation Solutions: Next Generation Solutions [Wiley Online]

(McMaster University), (University of Maryland, Baltimore County)
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Leading experts present the latest research results in adaptive signal processing Recent developments in signal processing have made it clear that significant performance gains can be achieved beyond those achievable using standard adaptive filtering approaches. Adaptive Signal Processing presents the next generation of algorithms that will produce these desired results, with an emphasis on important applications and theoretical advancements. This highly unique resource brings together leading authorities in the field writing on the key topics of significance, each at the cutting edge of its own area of specialty. It begins by addressing the problem of optimization in the complex domain, fully developing a framework that enables taking full advantage of the power of complex-valued processing. Then, the challenges of multichannel processing of complex-valued signals are explored. This comprehensive volume goes on to cover Turbo processing, tracking in the subspace domain, nonlinear sequential state estimation, and speech-bandwidth extension.





Examines the seven most important topics in adaptive filtering that will define the next-generation adaptive filtering solutions



Introduces the powerful adaptive signal processing methods developed within the last ten years to account for the characteristics of real-life data: non-Gaussianity, non-circularity, non-stationarity, and non-linearity



Features self-contained chapters, numerous examples to clarify concepts, and end-of-chapter problems to reinforce understanding of the material



Contains contributions from acknowledged leaders in the field





Adaptive Signal Processing is an invaluable tool for graduate students, researchers, and practitioners working in the areas of signal processing, communications, controls, radar, sonar, and biomedical engineering.
Preface xi
Contributors xv
Complex-Valued Adaptive Signal Processing
1(86)
Introduction
1(5)
Why Complex-Valued Signal Processing
3(2)
Outline of the
Chapter
5(1)
Preliminaries
6(25)
Notation
6(3)
Efficient Computation of Derivatives in the Complex Domain
9(8)
Complex-to-Real and Complex-to-Complex Mappings
17(3)
Series Expansions
20(4)
Statistics of Complex-Valued Random Variables and Random Processes
24(7)
Optimization in the Complex Domain
31(9)
Basic Optimization Approaches in Rn
31(3)
Vector Optimization in Cn
34(3)
Matrix Optimization in Cn
37(1)
Newton-Variant Updates
38(2)
Widely Linear Adaptive Filtering
40(7)
Linear and Widely Linear Mean-Square Error Filter
41(6)
Nonlinear Adaptive Filtering with Multilayer Perceptrons
47(11)
Choice of Activation Function for the MLP Filter
48(7)
Derivation of Back-Propagation Updates
55(3)
Complex Independent Component Analysis
58(16)
Complex Maximum Likelihood
59(5)
Complex Maximization of Non-Gaussianity
64(2)
Mutual Information Minimization: Connections to ML and MN
66(1)
Density Matching
67(4)
Numerical Examples
71(3)
Summary
74(2)
Acknowledgment
76(1)
Problems
76(11)
References
79(8)
Robust Estimation Techniques for Complex-Valued Random Vectors
87(56)
Introduction
87(4)
Signal Model
88(2)
Outline of the
Chapter
90(1)
Statistical Characterization of Complex Random Vectors
91(4)
Complex Random Variables
91(2)
Complex Random Vectors
93(2)
Complex Elliptically Symmetric (CES) Distributions
95(7)
Definition
96(2)
Circular Case
98(1)
Testing the Circularity Assumption
99(3)
Tools to Compare Estimators
102(5)
Robustness and Influence Function
102(4)
Asymptotic Performance of an Estimator
106(1)
Scatter and Pseudo-Scatter Matrices
107(7)
Background and Motivation
107(1)
Definition
108(2)
M-Estimators of Scatter
110(4)
Array Processing Examples
114(7)
Beamformers
114(1)
Subspace Methods
115(3)
Estimating the Number of Sources
118(2)
Subspace DOA Estimation for Noncircular Sources
120(1)
MVDR Beamformers Based on M-Estimators
121(7)
The Influence Function Study
123(5)
Robust ICA
128(9)
The Class of DOGMA Estimators
129(3)
The Class of GUT Estimators
132(2)
Communications Example
134(3)
Conclusion
137(1)
Problems
137(6)
References
138(5)
Turbo Equalization
143(68)
Introduction
143(1)
Context
144(1)
Communication Chain
145(2)
Turbo Decoder: Overview
147(5)
Basic Properties of Iterative Decoding
151(1)
Forward-Backward Algorithm
152(11)
With Intersymbol Interference
160(3)
Simplified Algorithm: Interference Canceler
163(5)
Capacity Analysis
168(5)
Blind Turbo Equalization
173(9)
Differential Encoding
179(3)
Convergence
182(13)
Bit Error Probability
187(3)
Other Encoder Variants
190(2)
EXIT Chart for Interference Canceler
192(2)
Related Analyses
194(1)
Multichannel and Multiuser Settings
195(4)
Forward-Backward Equalizer
196(1)
Interference Canceler
197(1)
Multiuser Case
198(1)
Concluding Remarks
199(1)
Problems
200(11)
References
206(5)
Subspace Tracking for Signal Processing
211(60)
Introduction
211(2)
Linear Algebra Review
213(6)
Eigenvalue Value Decomposition
213(1)
QR Factorization
214(1)
Variational Characterization of Eigenvalues/Eigenvectors of Real Symmetric Matrices
215(1)
Standard Subspace Iterative Computational Techniques
216(2)
Characterization of the Principal Subspace of a Covariance Matrix from the Minimization of a Mean Square Error
218(1)
Observation Model and Problem Statement
219(2)
Observation Model
219(1)
Statement of the Problem
220(1)
Preliminary Example: Oja's Neuron
221(2)
Subspace Tracking
223(10)
Subspace Power-Based Methods
224(6)
Projection Approximation-Based Methods
230(2)
Additional Methodologies
232(1)
Eigenvectors Tracking
233(10)
Rayleigh Quotient-Based Methods
234(1)
Eigenvector Power-Based Methods
235(5)
Projection Approximation-Based Methods
240(1)
Additional Methodologies
240(2)
Particular Case of Second-Order Stationary Data
242(1)
Convergence and Performance Analysis Issues
243(13)
A Short Review of the ODE Method
244(2)
A Short Review of a General Gaussian Approximation Result
246(2)
Examples of Convergence and Performance Analysis
248(8)
Illustrative Examples
256(4)
Direction of Arrival Tracking
257(1)
Blind Channel Estimation and Equalization
258(2)
Concluding Remarks
260(1)
Problems
260(11)
References
266(5)
Particle Filtering
271(62)
Introduction
272(2)
Motivation for Use of Particle Filtering
274(4)
The Basic Idea
278(11)
The Choice of Proposal Distribution and Resampling
289(6)
Choice of Proposal Distribution
290(1)
Resampling
291(4)
Some Particle Filtering Methods
295(10)
SIR Particle Filtering
295(2)
Auxiliary Particle Filtering
297(4)
Gaussian Particle Filtering
301(1)
Comparison of the Methods
302(3)
Handling Constant Parameters
305(5)
Kernel-Based Auxiliary Particle Filter
306(2)
Density-Assisted Particle Filter
308(2)
Rao-Blackwellization
310(4)
Prediction
314(2)
Smoothing
316(4)
Convergence Issues
320(3)
Computational Issues and Hardware Implementation
323(1)
Acknowledgments
324(1)
Exercises
325(8)
References
327(6)
Nonlinear Sequential State Estimation for Solving Pattern-Classification Problems
333(16)
Introduction
333(1)
Back-Propagation and Support Vector Machine-Learing Algorithms: Review
334(6)
Back-Propagation Learning
334(3)
Support Vector Machine
337(3)
Supervised Training Framework of MLPs Using Nonlinear Sequential State Estimation
340(1)
The Extended Kalman Filter
341(3)
The EKF Algorithm
344(1)
Experimental Comparison of the Extended Kalman Filtering Algorithm with the Back-Propagation and Support Vector Machine Learning Algorithms
344(3)
Concluding Remarks
347(1)
Problems
348(1)
References
348(1)
Bandwidth Extension of Telephony Speech
349(44)
Introduction
349(3)
Organization of the
Chapter
352(1)
Nonmodel-Based Algorithms for Bandwidth Extension
352(2)
Oversampling with Imaging
353(1)
Application of Nonlinear Characteristics
353(1)
Basics
354(10)
Source-Filter Model
355(3)
Parametric Representations of the Spectral Envelope
358(4)
Distance Measures
362(2)
Model-Based Algorithms for Bandwidth Extension
364(19)
Generation of the Excitation Signal
365(4)
Vocal Tract Transfer Function Estimation
369(14)
Evaluation of Bandwidth Extension Algorithms
383(5)
Objective Distance Measures
383(2)
Subjective Distance Measures
385(3)
Conclusion
388(1)
Problems
388(5)
References
390(3)
Index 393
TÜLAY ADALI, PhD, is Professor of Electrical Engineering and Director of the Machine Learning for Signal Processing Laboratory at the University of Maryland, Baltimore County. Her research interests are in statistical and adaptive signal processing, with emphasis on nonlinear and complex-valued signal processing, and applications in biomedical data analysis and communications. Simon Haykin, PhD, is Distinguished University Professor and Director of the Cognitive Systems Laboratory in the Faculty of Engineering at McMaster University. A world-renowned authority on adaptive and learning systems, Dr. Haykin has pioneered signal-processing techniques and systems for radar and communication applications, culminating in the study of cognitive dynamic systems, which has become his research passion.