A robust diffusion recursive generalized modified Blake-Zisserman algorithm for distributed estimation under an adaptive kernel width

Abstract

In adaptive networks, the performance of distributed estimation is degraded in the presence of non-Gaussian noises. A number of robust criteria for diffusion approaches, such as generalized maximum correntropy and hyperbolic cosine function have been developed towards non-Gaussian/impulsive background noises. However, these algorithms are affected by high steady-state misadjustment. Therefore, to improve the robustness under non-Gaussian noise and decrease steady-state misadjustment, a generalized modified Blake-Zisserman (GMBZ) robust loss function is represented in this study. Also, we propose a new robust diffusion recursive least squares (RLS) based on GMBZ. Additionally, to enhance tracking ability in non-stationary environments, the proposed method is extended by an adaptive strategy for kernel width selection. The convergence analysis and the steady-state performance of the proposed method are also discussed. Simulation results demonstrate the effectiveness and robustness of the proposed algorithms for system identification scenarios in the presence of α-stable noise in stationary and non-stationary environments.