Abstract
The diffusion LMS (DLMS) is one of the most popular online distributed estimation algorithms, due to its simplicity and ease of implementation. However, it may suffer from large steady-state misalignment in some strong, non-Gaussian noise environments. To address this problem, this paper introduces a diffusion least mean fourth (DLMF) algorithm by using the mean-fourth error cost function in a diffusion strategy. Moreover, a variable step-size (VSS) method is developed to further reduce the steady-state misalignment of the DLMF. Simulation results show that the DLMF outperforms the DLMS with uniform or binary noise, and that the VSS-DLMF has a superior steady-state performance as compared to the DLMF.
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