ROBUST DISTRIBUTED DETECTION OVER ADAPTIVE DIFFUSION NETWORKS

被引：0

作者：

Al-Sayed, Sara ^{[1
]}

Zoubir, Abdelhak M. ^{[1
]}

Sayed, Ali H. ^{[2
]}

机构：

[1] Tech Univ Darmstadt, Signal Proc Grp, D-64283 Darmstadt, Germany

[2] Univ Calif Los Angeles, Dept Elect Engn, Los Angeles, CA 90095 USA

来源：

2014 IEEE INTERNATIONAL CONFERENCE ON ACOUSTICS, SPEECH AND SIGNAL PROCESSING (ICASSP) | 2014年

关键词：

Adaptive networks; diffusion LMS; robust distributed detection; hypothesis testing; error nonlinearity; ADAPTATION; STRATEGIES;

D O I：

暂无

中图分类号：

O42 [声学];

学科分类号：

070206 ; 082403 ;

摘要：

Diffusion adaptation techniques based on the least-mean-squares criterion have been proposed for distributed detection of a signal in Gaussian-distributed noise, forgoing the need for a fusion center. However, least-mean-squares solutions are generally non-robust against impulsive noise. In this work, we combine nonlinear filtering with diffusion adaptation and propose a strategy for distributed detection in the presence of impulsive noise. The superiority of the algorithm is validated experimentally.

引用

页数：5

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