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Adaptive Filtering Algorithms and Practical Implementation

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ISBN-10: 1402071256

ISBN-13: 9781402071256

Edition: 2nd 2002 (Revised)

Authors: Paulo S. Diniz

List price: $104.00
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Description:

A concise overview of adaptive filtering, covering as many algorithms as possible in a unified form that avoids repetition and simplifies notation.
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Book details

List price: $104.00
Edition: 2nd
Copyright year: 2002
Publisher: Springer
Publication date: 7/31/2002
Binding: Hardcover
Pages: 592
Size: 6.25" wide x 9.50" long x 1.25" tall
Weight: 2.134
Language: English

Preface
Introduction to Adaptive Filtering
Introduction
Adaptive Signal Processing
Introduction to Adaptive Algorithms
Applications
Fundamentals of Adaptive Filtering
Introduction
Signal Representation
Deterministic Signals
Random Signals
Ergodicity
The Correlation Matrix
Wiener Filter
Linearly-Constrained Wiener Filter
The Generalized Sidelobe Canceller
Mean-Square Error Surface
Bias and Consistency
Newton Algorithm
Steepest-Descent Algorithm
Applications Revisited
System Identification
Signal Enhancement
Signal Prediction
Channel Equalization
Digital Communication System
Concluding Remarks
The Least-Mean-Square (LMS) Algorithm
Introduction
The LMS Algorithm
Some Properties of the LMS Algorithm
Gradient Behavior
Convergence Behavior of the Coefficient Vector
Coefficient-Error-Vector Covariance Matrix
Behavior of the Error Signal
Minimum Mean-Square Error
Excess Mean-Square Error and Misadjustment
Transient Behavior
LMS Algorithm Behavior in Nonstationary Environments
Examples
Analytical Examples
System Identification Simulations
Channel Equalization Simulations
Fast Adaptation Simulations
The Linearly-Constrained LMS Algorithm
Concluding Remarks
LMS-Based Algorithms
Introduction
Quantized-Error Algorithms
Sign-Error Algorithm
Dual-Sign Algorithm
Power-of-Two Error Algorithm
Sign-Data Algorithm
The LMS-Newton Algorithm
The Normalized LMS Algorithm
The Transform-Domain LMS Algorithm
The Affine Projection Algorithm
Simulation Examples
Signal Enhancement Simulation
Signal Prediction Simulation
Concluding Remarks
Conventional RLS Adaptive Filter
Introduction
The Recursive Least-Squares Algorithm
Properties of the Least-Squares Solution
Orthogonality Principle
Relation Between Least-Squares and Wiener Solutions
Influence of the Deterministic Autocorrelation Initialization
Steady-State Behavior of the Coefficient Vector
Coefficient-Error-Vector Covariance Matrix
Behavior of the Error Signal
Excess Mean-Square Error and Misadjustment
Behavior in Nonstationary Environments
Simulation Examples
Concluding Remarks
Adaptive Lattice-Based RLS Algorithms
Introduction
Recursive Least-Squares Prediction
Forward Prediction Problem
Backward Prediction Problem
Order-Updating Equations
A New Parameter [delta](k, i)
Order Updating of [xi superscript d subscript b subscript min](k, i) and w[subscript b](k, i)
Order Updating of [xi superscript d subscript f subscript min](k, i) and w[subscript f](k, i)
Order Updating of Prediction Errors
Time-Updating Equations
Time Updating for Prediction Coefficients
Time Updating for [delta](k, i)
Order Updating for [gamma](k, i)
Joint-Process Estimation
Time Recursions of the Least-Squares Error
Normalized Lattice RLS Algorithm
Basic Order Recursions
Feedforward Filtering
Error-Feedback Lattice RLS Algorithm
Recursive Formulas for the Reflection Coefficients
Lattice RLS Algorithm Based on A Priori Errors
Quantization Effects
Concluding Remarks
Fast Transversal RLS Algorithms
Introduction
Recursive Least-Squares Prediction
Forward Prediction Relations
Backward Prediction Relations
Joint-Process Estimation
Stabilized Fast Transversal RLS Algorithm
Concluding Remarks
QR-Decomposition-Based RLS Filters
Introduction
Triangularization Using QR-Decomposition
Initialization Process
Input data matrix triangularization
QR-Decomposition RLS Algorithm
Systolic Array Implementation
Some Implementation Issues
Fast QR-RLS Algorithm
Backward Prediction Problem
Forward Prediction Problem
Conclusions and Further Reading
Adaptive IIR Filters
Introduction
Output-Error IIR Filters
General Derivative Implementation
Adaptive Algorithms
Recursive least-squares algorithm
The Gauss-Newton algorithm
Gradient-based algorithm
Alternative Adaptive Filter Structures
Cascade Form
Lattice Structure
Parallel Form
Frequency-Domain Parallel Structure
Mean-Square Error Surface
Influence of the Filter Structure on MSE Surface
Alternative Error Formulations
Equation Error Formulation
The Steiglitz-McBride Method
Conclusion
Nonlinear Adaptive Filtering
Introduction
The Volterra Series Algorithm
LMS Volterra Filter
RLS Volterra Filter
Adaptive Bilinear Filters
Multilayer Perceptron Algorithm
Radial Basis Function Algorithm
Conclusion
Subband Adaptive Filters
Introduction
Multirate Systems
Decimation and Interpolation
Filter Banks
Two-Band Perfect Reconstruction Filter Banks
Analysis of Two-Band Filter Banks
Analysis of M-Band Filter Banks
Hierarchical M-Band Filter Banks
Cosine-Modulated Filter Banks
Block Representation
Subband Adaptive Filters
Subband Identification
Two-Band Identification
Closed-Loop Structure
Cross-Filters Elimination
Fractional Delays
Delayless Subband Adaptive Filtering
Computational Complexity
Frequency-Domain Adaptive Filtering
Conclusion
Quantization Effects in the LMS and RLS Algorithms
Quantization Effects in the LMS Algorithm
Error Description
Error Models for Fixed-Point Arithmetic
Coefficient-Error-Vector Covariance Matrix
Algorithm Stop
Mean-Square Error
Floating-Point Arithmetic Implementation
Floating-Point Quantization Errors in LMS Algorithm
Quantization Effects in the RLS Algorithm
Error Description
Error Models for Fixed-Point Arithmetic
Coefficient-Error-Vector Covariance Matrix
Algorithm Stop
Mean-Square Error
Fixed-Point Implementation Issues
Floating-Point Arithmetic Implementation
Floating-Point Quantization errors in RLS Algorithm