Blind signal processing

Author: Ma Jianfang, Niu Yilong, Chen Haiyang
Publisher:
Publish Date: 2006-06-01
Features: Blind signal processing is a rapidly developing important direction in modern digital signal processing and computational intelligence in recent years. It has broad application prospects in numerous fields such as electronic information, communication, biomedical, image enhancement, radar, and geophysical signal processing. This book systematically introduces the basic theory, analytical methods, fundamental models, various algorithms, and new research directions and methods in blind signal processing, including mathematical foundations of blind processing, principal component analysis, whitening preprocessing, blind identification and separation methods based on eigenvalue decomposition of the correlation matrix, blind source separation and independent component analysis, neural network methods for independent component analysis, BSS and ICA for nonlinear mixed signals, blind equalization and blind identification, and blind adaptive multi-user detection. The appendix lists Matlab programs for some blind processing algorithms. This book can serve as a textbook for senior undergraduate and graduate students, as well as a reference for scientists and engineers in related fields such as electronic information, communication, image processing, remote sensing, radar, biomedical signal processing, seismology, and speech signal processing.
Blind signal processing is a rapidly developing important direction in modern digital signal processing and computational intelligence in recent years. It has broad application prospects in numerous fields such as electronic information, communication, biomedical, image enhancement, radar, and geophysical signal processing. This book systematically introduces the basic theory, analytical methods, fundamental models, various algorithms, and new research directions and methods in blind signal processing, including mathematical foundations of blind processing, principal component analysis, whitening preprocessing, blind identification and separation methods based on eigenvalue decomposition of the correlation matrix, blind source sharing and independent component analysis, neural network methods for independent component analysis, BSS and ICA for nonlinear mixed signals, blind equalization and blind identification, and blind adaptive multi-user detection. The appendix lists Matlab programs for some blind processing algorithms. This book can serve as a textbook for senior undergraduate and graduate students, as well as a reference for scientists and engineers in related fields such as electronic information, communication, image processing, remote sensing, radar, biomedical signal processing, seismology, and speech signal processing.