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9780130402653

Theory and Design of Adaptive Filters

by ; ;
  • ISBN13:

    9780130402653

  • ISBN10:

    0130402656

  • Format: Paperback
  • Copyright: 2001-01-01
  • Publisher: Prentice Hall

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Supplemental Materials

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Summary

Rather than superficially examining an extensive list of possible applications benefiting from adaptive filter use, the authors examine four such problems in detail and review the common attributes that are shared with many other applications of adaptive filtering.The authors develop the basic rules and algorithms for filter performance and provide tools for design, along with an appreciation of the complexity of behavioral analysis. Derivations and convergence discussions are kept to a basic level. The presentation focuses on a few principles and applies them to a series of motivating examples, that include in-depth discussion of implementation aspects for filter design not found in other books.Serves as a valuable reference for practicing engineers.

Table of Contents

Preface xi
The Need for Adaptive Filtering
1(20)
Removal of Power Line HUM from Medical Instruments
2(2)
Equalization of Troposcatter Communication Signals
4(5)
The Modeling of Physical Processes
9(2)
Enhancing Reception Quality Using an Array of Antennas
11(4)
Generality and Commonality
15(2)
Problems
17(4)
Basic Principles of Adaptive Filtering
21(18)
The FIR Linear Combiner
22(2)
Number Guessing Games
24(11)
Single Integer Guessing Games
24(2)
Multiple Integer Guessing Games
26(6)
Multiple Real Numbers Guessing Games
32(3)
Adaptive Filter Algorithm Interpretation
35(2)
Problems
37(2)
An Analytical Framework for Developing Adaptive Algorithms
39(52)
Background and Direction
39(1)
The Least Squares Problem
40(15)
Basic Formulation
40(1)
Reduction to the Normal Equations
41(4)
Direct Solution for the Optimal Vector Woss
45(1)
The Meaning of Pss and Rss
46(1)
Examples
47(4)
Two Solution Techniques
51(3)
Consolidation
54(1)
The Least Mean Squares Problem
55(15)
Formulation
55(1)
A Brief Review of Stochastic Processes
55(7)
Development of the Normal Equations
62(1)
The Ensemble Average Auto- and Cross-Correlation Functions
63(1)
Examples
64(4)
Consolidation
68(2)
Properties of the Solution
70(16)
Evaluation of the Performance Function
70(1)
The Positivity of R
71(2)
Examples Revisited
73(1)
Decompositions Based on the Eigensystem of R
74(3)
A Geometrical View of the Squared-Error Performance Function
77(6)
Another Useful View---Spectral Decomposition of the Filter Input
83(3)
Summary and Perspective
86(2)
Problems
88(3)
Algorithms for Adapting FIR Filters
91(54)
Introduction
91(1)
Search Techniques
92(24)
The Gradient Search Approach
92(2)
Approximation of the Gradient
94(1)
The Average Convergence Properties of LMS
95(3)
The Effects of a Singular Autocorrelation Matrix
98(1)
Using the Input Signal Spectrum to Predict the Performance of an LMS-Adapted FIR Filter
99(3)
Related Approximate Gradient Search Algorithms
102(4)
Simplified and Modified Versions of the LMS Algorithm
106(4)
Gradient Noise, Weight Noise, and the Concept of Misadjustment
110(1)
The Effect of the Adaptation Constant on Tracking Performance
111(2)
The Concept of the Adaptive Learning Curve
113(3)
Recursive Solution Techniques
116(12)
The Motivation for Recursive Algorithms
116(1)
Recursive Least Squares (RLS)
116(8)
``Fast'' RLS Algorithms
124(4)
An Example Using Both LMS and RLS
128(5)
Summary and Perspective
133(2)
Problems
135(10)
Algorithms for Adapting IIR Filters
145(36)
Introduction
145(6)
A Justification of IIR Modeling
145(3)
Alternate Realizations and Altered Algorithms
148(1)
Adaptive IIR Filter Algorithm Construction
149(2)
Gradient Descent Minimization of Squared Prediction Error
151(4)
A Less Expensive Gradient Approximation
153(1)
Stability Check and Projection
154(1)
Parameter Identification Format and Stability Theory Interpretation
155(6)
Homogeneous Error System Stability Formulation
158(2)
Error Filtering
160(1)
Filtered-Error and Filtered-Regressor Algorithms
161(2)
Steiglitz-McBride Algorithm
163(4)
IIR Whitener
167(3)
ARMAX Modeling
170(3)
Summary
173(3)
Problems
176(5)
Adaptive Algorithms for Restoring Signal Properties
181(26)
Introduction
181(2)
Classical Approaches to Coping with the Lack of a Reference Waveform
183(3)
Using a Function of the Input Signal Itself as the Reference
183(1)
Prearrangement of a Suitable Reference Signal
184(1)
Using A Priori Statistical Knowledge about the Inputs to Avoid the Need for a Reference Signal
185(1)
The Property-Restoration Concept
186(1)
The Constant-Modulus Adaptive Algorithm
187(6)
The Filter Structure
188(1)
The Error Signal
188(1)
The Adaptive Algorithm
189(1)
An Example Using the Gradient Descent Version of CMA
190(3)
Extension of CMA to Data Signals
193(5)
``Constant Envelope'' Data Signals
193(2)
Quadrature-Amplitude Modulation (QAM) and Other Nonconstant-Envelope Signals
195(2)
Analytical Considerations in the Design of Property-Restoration Algorithms
197(1)
Summary
198(1)
Problems
199(8)
Implementation Issues
207(60)
Introduction
207(3)
Sizing Considerations
210(3)
Real-Time Operation
213(1)
Implementation Efficiencies
214(18)
Data Storage
214(1)
Symmetric Filtering
215(3)
Complex Filtering and Narrowband Signals
218(4)
Algorithmic Shortcuts
222(7)
Cyclostationary Filtering
229(2)
Algorithmic Leaking
231(1)
Frequency Domain Implementations
232(12)
Fast Output Convolution: A Complexity Analysis
232(4)
Fast Update Calculation
236(3)
Channelized Adaptation
239(1)
Designed Channelization
240(3)
Modal Decoupling Architecture
243(1)
Precision Effects
244(17)
Scaling
245(4)
Performance Issues
249(4)
Arithmetic Effects in Fixed Designs
253(5)
Arithmetic Considerations and Adaptation
258(3)
Analog Alternatives
261(2)
IIR Considerations
263(1)
Summary
264(3)
Design Example: Hum Removal for an Electrocardiagram Monitor
267(12)
Introduction
267(1)
Review of the Problem
267(2)
Design Approach
269(2)
The Adaptive Noise Canceller (ANC)
271(1)
Using the Adaptive Noise Canceller to Cancel Echo in Two-wire Telephone Modems
272(3)
Handling Interference from Multiple Sources
275(4)
Design Example: Multipath Correction for Troposcatter Signals
279(14)
The Problem
279(3)
Design Steps
282(6)
Incorporation of Diversity Combination
288(2)
The Extension to Bauded Signals
290(1)
Adaptive Channel Equalization for Terrestrial Digital Microwave Radios
290(1)
Adaptive Channel Equalization for Voiceband Modems
290(3)
Design Example: Modeling the Propagation Path for a Digital Television Signal
293(16)
Background
293(2)
The Basic Approach
295(1)
A More Advanced Approach
296(1)
Example
297(4)
Additional Considerations
301(2)
Using the Channel Model to Influence the Equalizer's Design
303(2)
The Difference Between the Filtering Problem and System Identification
305(2)
Final Comments
307(2)
Design Example: Enhancing Signal Reception Quality Using an Array of Antennas
309(16)
The Problem
309(1)
A Simplified Version of the Problem and a Solution
309(4)
More Complicated Filters to Deal with More Complicated Signals
313(4)
The Adaptive Linear Combiner
313(3)
Extension to Time and Space
316(1)
Adding Adaptivity to Spatial and Spatio-Temporal Processors
317(5)
The Need for Adaptivity
317(1)
System Level Choices
318(1)
A Simple Design Example
319(3)
Applications and Extensions
322(3)
Epilog
325(2)
Appendix A Adaptive Filtering in Matlab 327(10)
A.1 Structural Notes
327(2)
A.2 Examples
329(8)
A.2.1 Figure 8.2: LMS Hum Canceller
329(2)
A.2.2 Figure 4.5a: Learning Curve for LMS Line Canceller
331(2)
A.2.3 Figure 4.5b: Learning Curve for RLS Line Canceller
333(4)
Bibliography 337(8)
Index 345

Supplemental Materials

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The New copy of this book will include any supplemental materials advertised. Please check the title of the book to determine if it should include any access cards, study guides, lab manuals, CDs, etc.

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