A diffusion strategy for robust distributed estimation based on streaming graph signals

The problem of robust distributed estimation over dynamic and streaming graph signals is investigated in this paper. Existing works related to distributed estimation over dynamic and streaming graph signals are mainly derived from the Mean-Square-Error criterion, and they are vulnerable to nonGaussian noise. Therefore, a new kind of diffusion Mixture correntropy (d-MC) algorithm is developed to deal with the disadvantage in this paper. Incorporating the diffusion strategy and a novel cost function, the proposed algorithm could accurately estimate the graph filter parameter with the dynamic and streaming graph signals, and achieve desirable performance under both Gaussian and impulsive noise environment. Besides, the theoretical analysis results of mean and mean-square stability are derived. Simulations on various case studies indicate the desirable performance of proposed d-MC algorithm by comparing it to other benchmarks. (c) 2023 ISA. Published by Elsevier Ltd. All rights reserved.

Automated summary

This paper investigates the problem of robust distributed estimation over dynamic and streaming graph signals, and proposes a new d-MC algorithm to overcome the vulnerability of existing methods to nonGaussian noise based on the Mean-Square-Error criterion. Simulation results demonstrate the desirable performance of the proposed algorithm in various noise environments.