| Description |
1 online resource (1 volume) : illustrations |
| Contents |
Front cover; Title page; Copyright page; Dedication; Acknowledgements; Table of Contents; Preface; Chapter 1. Introduction; Adaptive signal processing; Examples of adaptive filtering; Adaptive system identification; Adaptive inverse system identification; Raison d'être for partial coefficient updates; Resource constraints; Convergence performance; System identification with white input signal; System identification with correlated input signal; Chapter 2. Approaches to partial coefficient updates; Introduction; Periodic partial updates; Example 1: Convergence performance |
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Example 2: Convergence difficultiesSequential partial updates; Example 1: Convergence performance; Example 2: Cyclostationary inputs; Example 3: Instability; Stochastic partial updates; System identification example; M -max updates; Example 1: Eigenvalue spread of RM; Example 2: Convergence performance; Example 3: Convergence rate and eigenvalues of RM; Example 4: Convergence difficulties; Example 5: Instability; Selective partial updates; Constrained optimization; Instantaneous approximation of Newton's method; q -Norm constrained optimization; Averaged system; Example 1: Eigenanalysis |
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Example 2: Convergence performanceExample 3: Instability; Set membership partial updates; Example 1: Convergence performance; Example 2: Instability; Block partial updates; Complexity considerations; Chapter 3. Convergence and stability analysis; Introduction; Convergence performance; Steady-state analysis; Partial-update LMS algorithms; Partial-update NLMS algorithms; Simulation examples for steady-state analysis; Convergence analysis; Partial-update LMS algorithms; Partial-update NLMS algorithms; Simulation examples for convergence analysis; Chapter 4. Partial-update adaptive filters |
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IntroductionLeast-mean-square algorithm; Partial-update LMS algorithms; Periodic-partial-update LMS algorithm; Sequential-partial-update LMS algorithm; Stochastic-partial-update LMS algorithm; M -max LMS algorithm; Computational complexity; Normalized least-mean-square algorithm; Partial-update NLMS algorithms; Periodic-partial-update NLMS algorithm; Sequential-partial-update NLMS algorithm; Stochastic-partial-update NLMS algorithm; M -max NLMS algorithm; Selective-partial-update NLMS algorithm; Set-membership partial-update NLMS algorithm; Computational complexity |
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Affine projection algorithmPartial-update affine projection algorithms; Periodic-partial-update APA; Sequential-partial-update APA; Stochastic-partial-update APA; M -max APA; Selective-partial-update APA; Set-membership partial-update APA; Selective-regressor APA; Computational complexity; Recursive least square algorithm; Partial-update RLS algorithms; Periodic-partial-update RLS algorithm; Sequential-partial-update RLS algorithm; Stochastic-partial-update RLS algorithm; Selective-partial-update RLS algorithm; Set-membership partial-update RLS algorithm; Partial-update RLS simulations |
| Summary |
Partial update adaptive signal processing algorithms not only permit significant complexity reduction in adaptive filter implementations, but also can improve the adaptive filter performance in telecommunications and image and video processing applications. This book gives state-of-art methods for the design and development of partial update adaptive signal processing algorithms for use in systems development. The book gives a comprehensive coverage of key partial updating schemes, giving detailed analysis and applications to noise cancellation, channel equalization, multiuser detection |
| Bibliography |
Includes bibliographical references and index |
| Notes |
Print version record |
| Subject |
Adaptive signal processing.
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Adaptive filters.
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Adaptive filters -- Design and construction
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Algorithms.
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algorithms.
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Adaptive filters
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Adaptive signal processing
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Algorithms
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| Form |
Electronic book
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| ISBN |
9780080921150 |
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0080921159 |
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0123741963 |
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9780123741967 |
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