Adaptive Filter Algorithms for Complex and Quaternion Data.

Format:

Book

Author/Creator:

Paul, Thomas K.

Series:

Engineering Series

Language:

English

Physical Description:

1 online resource (266 pages)

Edition:

1st ed.

Place of Publication:

Cham : Springer, 2026.

Summary:

This book provides readers with a thorough exposition of adaptive filter algorithms applicable for nonlinear systems, kernel-based systems and for complex and quaternionic data.The authors describe in detail the algorithms and methods used for adaptive filtering in these circumstances.

Contents:

Adaptive Filter Algorithms For Complex and Quaternion Data

Preface

Contents

1. Introduction

2. Linear and Kernel Filter Theory

3. Complex Data for Adaptive Filters

4. The Complex Kernel Least Mean Square Algorithm

5. Learning with Quaternions

6. Information Theoretic Learning

7. The Complex Kernel Affine Projection Algorithm

8. Extending Complex Kernel Filters with Widely Linear Estimation

9. Convergence Analysis of Complex Kernel LMS Algorithm

10. The Quaternion Kernel LMS Algorithm

11. The Quaternion Kernel Maximum Correntropy Algorithm

Appendix A: Associated RKHS of the Real Gaussian Kernel

Appendix B: Computation, Performance Tradeoff of CKLMS Versus CLMS

Appendix C: Convergence of CKLMS: Finding Rκκ, Complexified Kernel

Appendix D: CKLMS Stability/Steady-State Analysis, Complexified Kernel

Appendix E: Gaussian Kernel for Quaternion Data

Appendix F: Convergence of the Quat-KLMS and Quat-KMC Algorithms

F.1 Convergence of Quat-KLMS and WL Form

F.2 Convergence of IQuat-KLMS

F.3 Convergence of Quat-KMC

References

Index.

Notes:

Description based on publisher supplied metadata and other sources.

ISBN:

9783032237989

OCLC:

1609868715