Diffusion Normalized Maximum Versoria Criterion Robust to Impulsive Noise

被引：6
作者：
Zandi, Sajad [1 ]
Korki, Mehdi [2 ]
机构：
[1] Univ Malayer, Dept Elect Engn, Malayer 6571995863, Iran
[2] Swinburne Univ Technol, Sch Sci Comp & Engn Technol, Hawthorn, Vic 3122, Australia
来源：
IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS II-EXPRESS BRIEFS | 2023年 / 70卷 / 04期
关键词：
Steady-state; Convergence; Estimation; Robustness; Behavioral sciences; Noise measurement; Measurement uncertainty; Distributed estimation; diffusion-normalized maximum versoria criterion (d-NMVC); impulsive noise; DISTRIBUTED ESTIMATION; LMS ALGORITHM; FORMULATION; STRATEGIES; SQUARES;
D O I：
10.1109/TCSII.2022.3230831
中图分类号：
TM [电工技术]; TN [电子技术、通信技术];
学科分类号：
0808 ; 0809 ;
摘要：
In this brief, we propose a new diffusion normalized maximum Versoria criterion (d-NMVC) algorithm, which is based on maximization of the normalized maximum Versoria criterion (MVC) cost function to enhance the performance of the distributed estimation over networks in the presence of non-Gaussian noise. Convergence of the proposed algorithm, in the mean square sense and evolution behavior, under impulsive noise environment is also analyzed. Simulation results show the robustness of the proposed algorithm under impulsive noise environment against various non-Gaussian noise distributions.
引用
收藏
页码：1660 / 1664
页数：5
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