Advanced Concepts in Adaptive Signal Processing

Advanced Concepts in Adaptive Signal Processing

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Although adaptive filtering and adaptive array processing began with research and development efforts in the late 1950's and early 1960's, it was not until the publication of the pioneering books by Honig and Messerschmitt in 1984 and Widrow and Stearns in 1985 that the field of adaptive signal processing began to emerge as a distinct discipline in its own right. Since 1984 many new books have been published on adaptive signal processing, which serve to define what we will refer to throughout this book as conventional adaptive signal processing. These books deal primarily with basic architectures and algorithms for adaptive filtering and adaptive array processing, with many of them emphasizing practical applications. Most of the existing textbooks on adaptive signal processing focus on finite impulse response (FIR) filter structures that are trained with strategies based on steepest descent optimization, or more precisely, the least mean square (LMS) approximation to steepest descent. While literally hundreds of archival research papers have been published that deal with more advanced adaptive filtering concepts, none of the current books attempt to treat these advanced concepts in a unified framework. The goal of this new book is to present a number of important, but not so well known, topics that currently exist scattered in the research literature. The book also documents some new results that have been conceived and developed through research conducted at the University of Illinois during the past five years.
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Product details

  • Hardback | 307 pages
  • 154.94 x 236.22 x 27.94mm | 725.74g
  • Dordrecht, Netherlands
  • English
  • 1996 ed.
  • XIII, 307 p.
  • 0792397401
  • 9780792397403

Table of contents

Preface. 1: Introduction and Background. 1.1. Common Adaptive Concepts from Different Disciplines. 1.2. Generic Applications of Adaptive Methods. 1.3. Performance Measures in Adaptive Systems. 1.4. The Minimum Mean Squared Error Solution. 1.5. Adaptive Algorithms for FIR Systems. 1.6. Adaptive Algorithms for IIR Systems. 1.7. New Horizons in Adaptive Signal Processing. 1.8. Notation and Conventions. 2: Advanced Algorithms for 1-D Adaptive Filtering. 2.2. Data- Reusing LMS Algorithms. 2.3. Orthogonalization by PR Modulation. 2.3. The Gauss-Newton Adaptive Filtering algorithm. 2.4. Block Adaptive IIR Filters Using the PCG Method. 3: Structures and Algorithms for Two-Dimensional Adaptive Signal Processing. 3.1. Applications of Two-Dimensional Adaptive Filtering. 3.2. Two- Dimensional FIR Adaptive Filtering. 3.3. Two-Dimensional IIR Adaptive Filters. 3.4. Two-Dimensional IIR Adaptive Filtering Experiments. 3.5. Uniqueness Characteristics of the 2-D IIR MSE Minimization. 4: Adaptive Fault Tolerance. 4.1. Application of AFT to FIR Adaptive Filters. 4.2. Adaptive Filter Structures. 4.3. A Simple Fault Tolerant FIR Adaptive Filter. 4.4. The Transform Domain FTAF. 4.5. The DFT-Based TDFTAF with the Conjugate Gradient. 4.6. Robust and Practical TDFTAFs. 4.7. Full Fault Tolerance Transforms. 4.8. Discussion. 5: Adaptive Polynomial Filters. 5.1. The Volterra Series. 5.2. Gradient Based Adaptive Volterra Filters. 5.3. RLSSecond-Order Volterra Adaptive Filter. 5.4. LS Lattice Second-Order Volterra Adaptive Filter. 5.5. QR-Based LS Lattice Second Order Volterra Filter. 5.6. The Adaptive Volterra Filter for Gaussian Signals. 5.7. Other Polynomial-Based Nonlinear Adaptive Filters. 5.8. Discussion. Appendix. Subject Index.
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