TRACKING ANALYSES OF M-ESTIMATE BASED LMS AND NLMS ALGORITHMS

被引：4
|
作者：
Yu, Yi [1 ]
de Lamare, Rodrigo C. [2 ,3 ]
Yang, Tao [1 ]
Cai, Qiangming [1 ]
机构：
[1] Southwest Univ Sci & Technol, Sch Informat Engn, Robot Technol Used Special Environm Key Lab Sichu, Mianyang, Sichuan, Peoples R China
[2] Pontificia Univ Catolica Rio de Janeiro, CETUC, BR-22451900 Rio de Janeiro, Brazil
[3] Univ York, Dept Elect Engn, York YO10 5DD, N Yorkshire, England
来源：
2021 IEEE STATISTICAL SIGNAL PROCESSING WORKSHOP (SSP) | 2021年
基金：
中国国家自然科学基金;
关键词：
Adaptive filters; tracking performance; LMM and NLMS algorithms; impulsive noise; MEAN M-ESTIMATE; PERFORMANCE ANALYSIS; ADAPTIVE FILTER; IMPULSIVE NOISE; STEADY-STATE;
D O I：
10.1109/SSP49050.2021.9513747
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 paper, the tracking behaviors of the least mean M-estimate (LMM) and normalized LMM algorithms are analyzed in a unified manner in a non-stationary system described by the random-walk model. In the analysis, we consider the presence of impulsive noise and do not impose a specific distribution on the input signal. For predicting the steady-state performance, we provide analytical expressions for the algorithms. Unlike for the stationary case, the steady-state performance for the non-stationary case does not always improve as the step size decreases. As such, the optimal step size is also derived to reach the best steady-state performance. Simulation results support the theoretical findings.
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页码：1 / 5
页数：5
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