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A robust diffusion recursive generalized modified Blake-Zisserman algorithm for distributed estimation under an adaptive kernel width
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AI
DOI：10.1016/j.sigpro.2023.109009.png)
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Abstract
En 中文
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 back-ground 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 general-ized 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 ker-nel 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 al-gorithms for system identification scenarios in the presence of alpha-stable noise in stationary and non -stationary environments.(c) 2023 Elsevier B.V. All rights reserved.
Keywords:
Diffusion robust recursive least squares
Generalized modified Blake-Zisserman loss function
Adaptive kernel width
Theoretical analysis