A globally convergent approach for blind MIMO adaptive deconvolution

被引：35

作者：

Touzni, A ^{[1
]}

Fijalkow, I

Larimore, MG

Treichler, JR

机构：

[1] Univ Cergy Pontoise, Equipe Traitement Singal & Images, ENSEA, ETIS, Cergy Pontoise, France

[2] Appl Signal Technol Inc, Sunnyvale, CA 94086 USA

来源：

IEEE TRANSACTIONS ON SIGNAL PROCESSING | 2001年 / 49卷 / 06期

关键词：

blind adaptive source separation; constant modulus criterion; multiple input/multiple output convolutional systems;

D O I：

10.1109/78.923299

中图分类号：

TM [电工技术]; TN [电子技术、通信技术];

学科分类号：

0808 ; 0809 ;

摘要：

We discuss the blind deconvolution of multiple input/multiple output (MIMO) linear convolutional mixtures and propose a set of hierarchical criteria motivated by the maximum entropy principle, The proposed criteria are based on the constant-modules (CM) criterion in order to guarantee that all minima achieve perfectly restoration of different sources. The approach is moreover robust to errors in channel order estimation. practical implementation is addressed by a stochastic adaptive algorithm with a low computational cost. Complete convergence proofs, based on the characterization of all extrema, are provided. The efficiency of the proposed method is illustrated by numerical simulations.

引用

页码：1166 / 1178

页数：13

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