Robust Incremental 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 others. Energy efficiency, high estimation accuracy, and fast convergence are important goals in distributed estimation algorithms for WSN. This paper studies the problem of robust adaptive estimation in impulsive noise environment using robust cost function like Wilcox on norm and error saturation nonlinearity. The incremental 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 or outliers. In this paper the robust norm is introduced in incremental cooperative distributed network to estimate the desired parameters in presence of Gaussian contaminated impulsive noise.