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|Title:||Error saturation nonlinearities for robust incremental LMS over wireless sensor networks|
|Keywords:||B.8.2 [performance and reliability]: performance analysis and design aids|
C.2.4 [distributed systems]: distributed applications
Contaminated Gaussian noise
Distributed signal processing
G.3 [probability and statistics]: correlation and regression analysis
I.5.4 [applications]: signal processing
I.6.8 [types of simulation]: distributed, Monte Carlo
Saturation nonlinearity incremental leastmean squares algorithm
Wireless sensor networks
|Abstract:||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. � 2014 ACM.|
|Appears in Collections:||Research Publications|
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