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Diffusion MEEF with Variable-Center Algorithm Optimized by Joint Probability Density Matching and Entropy Weight Method
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DOI：10.1007/s00034-025-03344-x.png)
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Abstract
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The minimum error entropy with fiducial points (MEEF) criterion has received increasing attention for its robustness in dealing with complex non-Gaussian noise environments. However, traditional MEEF often assumes that the center of the error distribution is fixed limiting performance under non-zero mean noise distribution. To address this issue, a variable-center MEEF (MEEF-VC) criterion is first defined in this paper, which can adaptively adjust the reference center of entropy estimation according to the characteristics of the error distribution to effectively capture non-zero-mean noise and dynamic error variations. Then, a novel robust diffusion MEEF-VC (DMEEF-VC) algorithm is developed by Adapt-Then-Combine strategy for distributed estimation over network. Moreover, the key parameters in DMEEF-VC (including bandwidth, center position and weighting factor) are optimized adaptively by probability density matching and entropy weight method. Simulation results show that the proposed DMEEF-VC exhibits robustness and accuracy enhancement in diffusion-based adaptive signal processing tasks compared with the traditional robust diffusion adaptive filtering algorithm.
Keywords:
Minimum error entropy with fiducial points
Diffusion adaptive filter
Variable center
Entropy weight method
Robust distributed estimation
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