Robust Diffusion LMS over Wireless Sensor Network in Impulsive Noise

Distributed wireless sensor networks have been proposed as a solution to environment sensing, target tracking, data collection and other applications. Energy efficiency, high estimation accuracy, and fast convergence are important goals in distributed estimation algorithms for sensor network. This paper studies the problem of robust adaptive estimation in impulsive noise environment using robust cost function like Wilcoxon norm and saturation nonlinearity. The diffusion cooperative scheme conventionally used in sensor network in which each node have local computing ability and share them with their predefined neighbors, is not robust to impulsive type of noise. In this paper the robust norm is introduced in diffusion cooperative distributed network to estimate the desired parameters in presence of Gaussian contaminated impulsive noise. The simulation study shows that Wilcoxon norm and saturation linearity based diffusion LMS is robust to impulsive noise. KeywordsAdaptive networks, contaminated Gaussian distribution, distributed processing, incremental algorithm, diffusion LMS, Wilcoxon norm, error saturation nonlinearity algorithm.