Diffusion least mean square/fourth algorithm for distributed estimation

作者：Zheng, Zongsheng; Liu, Zhigang^*; Huang, Ming

来源：Signal Processing, 2017, 134: 268-274.

DOI：10.1016/j.sigpro.2016.12.020

• Adaptive networks
• Distributed estimation
• Diffusion strategy
• Mixed square/fourth error cost function

Proposed is a diffusion least mean square/fourth (LMS/F) algorithm, which is characterized by its fast convergence and low steady-state misalignment for distributed estimation in non-Gaussian noise environments. Instead of the conventional mean square error cost function, the diffusion LMS/F algorithm is derived from the mixed square/fourth error cost function, which is more suitable for non-Gaussian noise environments. Moreover, we incorporate the L1- and.4-norm constraints into the mixed square/fourth error cost function, and then a class of diffusion sparse LMS/F algorithms is developed which is able to exploit the sparsity of the considered system. Simulation results show that the diffusion LMS/F algorithm outperforms the conventional diffusion LMS and LMF algorithms in non-Gaussian noise environments. The improvements of diffusion sparse LMS/F algorithms in terms of steady-state misalignment are also demonstrated relative to the diffusion LMS/F algorithm.

• 出版日期2017-5
• 单位西南交通大学