Robust Diffusion Huber-Based Normalized Least Mean Square Algorithm with Adjustable Thresholds

To improve the performance of the diffusion Huber-based normalized least mean square algorithm in the presence of impulsive noise, this paper proposes a distributed recursion scheme to adjust the thresholds. Because of the decreasing characteristic of the thresholds, the proposed algorithm can also be interpreted as a robust diffusion normalized least mean square algorithm with variable step sizes so that it has not only fast convergence but also small steady-state estimation error. Based on the contaminated Gaussian model, we analyze the mean square behavior of the algorithm in impulsive noise. Moreover, to ensure good tracking capability of the algorithm for the sudden change of parameters of interest, a control strategy is given that resets the thresholds with their initial values. Simulations in various noise scenarios show that the proposed algorithm performs better than many existing diffusion algorithms.