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.
引用
下载
收藏
页码：1 / 5
页数：5
相关论文
共 50 条
  • [21] A robust spline filter algorithm based on M-estimate theory
    Zhang, Hao
    Zhang, Jing
    Hua, Jin
    Cheng, Yuzhu
    ENGINEERING SOLUTIONS FOR MANUFACTURING PROCESSES, PTS 1-3, 2013, 655-657 : 909 - +
  • [22] THE BEHAVIOR OF LMS AND NLMS ALGORITHMS IN THE PRESENCE OF SPHERICALLY INVARIANT PROCESSES
    RUPP, M
    IEEE TRANSACTIONS ON SIGNAL PROCESSING, 1993, 41 (03) : 1149 - 1160
  • [23] Comprehensive Analysis of LMS and NLMS algorithms using Adaptive Equalizers
    Teja, A.
    Meghashyam, B. K.
    Verma, C. Anubha
    2014 INTERNATIONAL CONFERENCE ON COMMUNICATIONS AND SIGNAL PROCESSING (ICCSP), 2014,
  • [24] Modified LMS and NLMS algorithms with non-negative weights
    Bershad, Neil J.
    Bermudez, Jose C. M.
    SIGNAL PROCESSING, 2024, 223
  • [25] Modified LMS and NLMS algorithms with a new variable step size
    Zhao Shengkui
    Man Zhihong
    Khoo Suiyang
    2006 9TH INTERNATIONAL CONFERENCE ON CONTROL, AUTOMATION, ROBOTICS AND VISION, VOLS 1- 5, 2006, : 1078 - +
  • [26] M-estimate based robust Kalman filter with application to integrated navigation
    Zhang Xiao-fang
    Wang De-chun
    PROCEEDINGS OF 2005 CHINESE CONTROL AND DECISION CONFERENCE, VOLS 1 AND 2, 2005, : 709 - +
  • [27] M-estimate Affine Projection Algorithm Based On Correntropy Induced Metric
    Liu, Bing
    Zhao, Haiquan
    IFAC PAPERSONLINE, 2019, 52 (24): : 107 - 110
  • [28] M-Estimate for the stationary hyperbolic GARCH models
    Lanciné Bamba
    Ouagnina Hili
    Abdou Kâ Diongue
    Assi N’Guessan
    METRON, 2021, 79 : 303 - 351
  • [29] Unified Form for the Robust Gaussian Information Filtering Based on M-Estimate
    Chang, Lubin
    Li, Kailong
    IEEE SIGNAL PROCESSING LETTERS, 2017, 24 (04) : 412 - 416
  • [30] M-Estimate for the stationary hyperbolic GARCH models
    Bamba, Lancine
    Hili, Ouagnina
    Diongue, Abdou Ka
    N'Guessan, Assi
    METRON-INTERNATIONAL JOURNAL OF STATISTICS, 2021, 79 (03): : 303 - 351
12345