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Báo cáo hóa học: " Research Article Blind Deconvolution in Nonminimum Phase Systems Using Cascade Structure"

Tuyển tập báo cáo các nghiên cứu khoa học quốc tế ngành hóa học dành cho các bạn yêu hóa học tham khảo đề tài: Research Article Blind Deconvolution in Nonminimum Phase Systems Using Cascade Structure | Hindawi Publishing Corporation EURASIP Journal on Advances in Signal Processing Volume 2007 Article ID 48432 10 pages doi 10.1155 2007 48432 Research Article Blind Deconvolution in Nonminimum Phase Systems Using Cascade Structure Bin Xia and Liqing Zhang Department of Computer Science and Engineering Shanghai Jiao Tong University Shanghai 200030 China Received 27 September 2005 Revised 11 June 2006 Accepted 16 July 2006 Recommended by Andrzej Cichocki We introduce a novel cascade demixing structure for multichannel blind deconvolution in nonminimum phase systems. To simplify the learning process we decompose the demixing model into a causal finite impulse response FIR filter and an anticausal scalar FIR filter. A permutable cascade structure is constructed by two subfilters. After discussing geometrical structure of FIR filter manifold we develop the natural gradient algorithms for both FIR subfilters. Furthermore we derive the stability conditions of algorithms using the permutable characteristic of the cascade structure. Finally computer simulations are provided to show good learning performance of the proposed method. Copyright 2007 Hindawi Publishing Corporation. All rights reserved. 1. INTRODUCTION Recently blind deconvolution has attracted considerable attention in various fields such as neural network wireless telecommunication speech and image enhancement biomedical signal processing EEG MEG signals 1-4 . Blind deconvolution is to retrieve the independent source signals from sensor outputs using only sensor signals and certain knowledge on statistics of source signals. A number of methods 2 5-13 have been developed for the blind deconvolution problem. For blind deconvolution problem in minimum phase systems causal filters are used as demixing models. Many algorithms work well in learning the coefficients of causal filters such as the second-order statistical SOS approaches 2 5-11 13 higher-order statistical HOS approaches 2 5 9 10 and the Bussgang .