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Diffusion Generalized Minimum Total Error Entropy Algorithm
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DOI：10.1109/LSP.2025.3533206.png)
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Abstract
En 中文
Both the minimum error entropy (MEE) and mixture MEE (MMEE) are extensively employed in distributed adaptive filters, exhibiting their robustness against non-Gaussian noise by capturing high-order statistical information from network data. However, the fixed shape of the Gaussian kernel function existing in MEE and MMEE restricts their flexibility, leading to reduced robustness and deteriorated performance. To address this issue, a novel diffusion generalized minimum total error entropy (DGMTE) algorithm is first proposed in this letter, using a generalized MEE criterion to significantly improve the performance of error-in-variables models-based algorithms under non-Gaussian noise. Moreover, as a special case of DGMTE, a generalized minimum total error entropy (GMTE) algorithm is also proposed, and the local convergence analysis of DGMTE is given. Finally, simulations show the superiorities of DGMTE in comparison with other representative algorithms.
Keywords:
Noise
Signal processing algorithms
Entropy
Shape
Adaptation models
Kernel
Estimation
Distributed algorithms
Cost function
Convergence
Adaptive filter
distributed estimation
errors-in-variables model
generalized minimum error entropy
total least squares
Journal
IF:
9.6
Papers:
1.1W
Citations:
1.7W
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