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Error Saturation Nonlinearities for Robust Incremental LMS over Wireless Sensor Networks
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DOI：10.1145/2629667.png)
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The data collected by sensor nodes over a geographical region is contaminated with Gaussian and impulsive noise. The conventional gradient-based distributed adaptive estimation algorithms exhibit good performance in the presence of Gaussian noise but perform poorly in impulsive noise environments. Therefore, the objective of this article is to propose a robust distributed adaptive algorithm that alleviates the effect of impulsive noise. An error saturation nonlinearity-based robust distributed strategy is proposed in an incremental cooperative network to estimate the desired parameters in impulsive noise. The steady-state analysis of the proposed error saturation nonlinearity incremental leastmean squares (SNILMS) algorithm is carried out by employing the spatial-temporal energy conservation principle. Both theoretical and simulation results show that the presence of the error nonlinearity has made the proposed SNILMS algorithm robust to impulsive noise.
Keyword:
Distributed Algorithms
Robust Estimation
Wireless sensor networks
contaminated Gaussian noise
distributed signal processing
incremental algorithm
saturation nonlinearity incremental leastmean squares algorithm
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