Unsupervised Adaptive Filtering, Blind Deconvolution
A complete, one-stop reference on the state of the art of unsupervised adaptive filtering While unsupervised adaptive filtering has its roots in the 1960s, more recent advances in signal processing, information theory, imaging, and remote sensing have made this a hot area for research in several diverse fields. This book brings together cutting-edge information previously available only in disparate papers and articles, presenting a thorough and integrated treatment of the two major classes of algorithms used in the field, namely, blind signal separation and blind channel equalization algorithms. Divided into two volumes for ease of presentation, this important work shows how these algorithms, although developed independently, are closely related foundations of unsupervised adaptive filtering. Through contributions by the foremost experts on the subject, the book provides an up-to-date account of research findings, explains the underlying theory, and discusses potential applications in diverse fields. More than 100 illustrations as well as case studies, appendices, and references further enhance this excellent resource. Following coverage begun in Volume I: Blind Source Separation, this volume discusses: The core of FSE-CMA behavior theory Relationships between blind deconvolution and blind source separation Blind separation of independent sources based on multiuser kurtosis optimization criteria
Why Read This Book
You should read this book if you need a deep, unified treatment of blind deconvolution and related unsupervised adaptive filtering algorithms, with both theoretical foundations and algorithmic detail. It consolidates decades of research (HOS, ICA, information-theoretic and adaptive approaches) and shows how to apply them to blind equalization, source separation, and convolutive mixtures.
Who Will Benefit
Graduate students, researchers, and senior engineers working on blind source separation, blind channel equalization, or advanced adaptive DSP for communications, audio, or imaging.
Level: Advanced — Prerequisites: Solid understanding of linear algebra, probability and stochastic processes, signals & systems, classical adaptive filtering (LMS/RLS) and familiarity with digital signal processing fundamentals; MATLAB experience helpful for experiments.
Key Takeaways
- Explain the theory of identifiability and conditions for successful blind deconvolution and separation.
- Derive and implement core ICA and blind equalization algorithms (Infomax, natural gradient, FastICA, JADE, Bussgang-based methods).
- Apply higher-order statistics and cumulant-based techniques to design blind adaptive filters for instantaneous and convolutive mixtures.
- Analyze performance and convergence properties of blind adaptive algorithms in noisy and colored-channel scenarios.
- Adapt frequency-domain and time-domain strategies to handle convolutive mixtures and multi-channel deconvolution problems.
Topics Covered
- 1. Introduction and historical overview of unsupervised adaptive filtering
- 2. Statistical foundations: moments, cumulants, and higher-order statistics
- 3. Identifiability and uniqueness in blind deconvolution and source separation
- 4. Instantaneous mixture models and basic ICA principles
- 5. Information-theoretic approaches (Infomax, ML) and contrast functions
- 6. Adaptive algorithms: natural gradient, Infomax, FastICA, and Bussgang methods
- 7. Blind channel equalization and single-channel deconvolution
- 8. Convolutive mixtures and frequency-domain techniques
- 9. Cumulant- and higher-order-statistics-based deconvolution methods
- 10. Practical issues: preconditioning, initialization, permutation/scaling ambiguities
- 11. Performance analysis, robustness to noise, and convergence behaviour
- 12. Applications: communications, audio/speech separation, radar/imaging examples
- 13. Case studies, simulations, and implementation notes
- Appendices, bibliography, and index
Languages, Platforms & Tools
How It Compares
Covers much of the same ground as Hyv arinen et al.'s 'Independent Component Analysis' but emphasizes adaptive-filtering and blind equalization perspectives; complements Comon and Jutten's edited works by providing a more unified algorithmic/adaptive view.
