Diffusion Generalized Minimum Total Error Entropy Algorithm

作     者：Cai, Peng Lin, Dongyuan Qian, Junhui Zheng, Yunfei Wang, Shiyuan 

作者机构：Southwest Univ Coll Elect & Informat Engn Chongqing 400715 Peoples R China Chongqing Univ Sch Microelect & Commun Engn Chongqing 400044 Peoples R China Chongqing Univ Chongqing Key Lab Biopercept & Intelligent Informa Chongqing 400044 Peoples R China 

出 版 物：《IEEE SIGNAL PROCESSING LETTERS》 (IEEE Signal Process Lett)

年 卷 期：2025年第32卷

页      面：751-755页

核心收录：

学科分类：0808[工学-电气工程] 08[工学] 

基　　金：National Natural Science Foundation of China [62471406, 62306245] Graduate Research and Innovation Project of Southwest University [SWUB24072] Graduate Research and Innovation Project of Chongqing [CYB23144] 

主　　题：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 

摘      要：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.