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Abstract:
Distributed adaptive learning algorithms have played a critical role in signal processing and parameter estimation over networks. Most existing algorithms are based on the mean-square error (MSE) criterion, and they can achieve desirable performance when the noise is modeled as Gaussian. However, the performance of MSE-based algorithms may degrade dramatically with the impulsive noise. Therefore, the aim of this paper is to present a diffusion algorithm, named generalized correntropy-based logarithmic difference (d-GCLD) algorithm, for distributed estimation that incorporates robustness to wireless sensor networks (WSNs). By combining the logarithm operation and the correntropy criterion as the loss function, the proposed algorithm is robust to impulsive noise and achieves satisfactory performance in various situations. In addition, the stability problem is studied theoretically. Experimental results are given to demonstrate the validity of the new algorithm in different scenarios.
