Constrained least lncosh adaptive filtering algorithm

被引:17
|
作者
Liang, Tao [1 ]
Li, Yingsong [1 ]
Zakharov, Yuriy, V [2 ]
Xue, Wei [1 ]
Qi, Junwei [1 ]
机构
[1] Harbin Engn Univ, Coll Informat & Commun Engn, Harbin 150001, Peoples R China
[2] Univ York, Dept Elect Engn, York YO10 5DD, N Yorkshire, England
来源
SIGNAL PROCESSING | 2021年 / 183卷
基金
中国博士后科学基金;
关键词
Constrained adaptive filtering; Lncosh cost function; System identification; Steady-state mean square analysis; Impulsive noise; SQUARE ERROR ANALYSIS; SYSTEM-IDENTIFICATION; CONVERGENCE ANALYSIS; CORRENTROPY; ORDER; LMS;
D O I
10.1016/j.sigpro.2021.108044
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
We propose a constrained least lncosh (CLL) adaptive filtering algorithm, which, as we show, provides better performance than other algorithms in impulsive noise environment. The proposed CLL algorithm is derived via incorporating a lncosh function in a constrained optimization problem under non-Gaussian noise environment. The lncosh cost function is a natural logarithm of a hyperbolic cosine function, and it can be considered as a combination of mean-square error and mean-absolute-error criteria. The theoretical analysis of convergence and steady-state mean-squared-deviation of the CLL algorithm in identification scenarios is presented. The theoretical analysis agrees well with simulation results and these results verify that the CLL algorithm possesses superior performance and higher robustness than other CAF algorithms under various non-Gaussian impulsive noises. (C) 2021 Elsevier B.V. All rights reserved.
引用
收藏
页数:9
相关论文
共 50 条
  • [41] Development of High Performance Quantum Image Algorithm on Constrained Least Squares Filtering Computation
    Wang, Shumei
    Xu, Pengao
    Song, Ruicheng
    Li, Peiyao
    Ma, Hongyang
    [J]. ENTROPY, 2020, 22 (11) : 1 - 13
  • [42] Adaptive Step-size Normalised Least Mean Square Algorithm for Spline Adaptive Filtering
    Sitjongsataporn, Suchada
    Chimpat, Worawut
    [J]. 2019 34TH INTERNATIONAL TECHNICAL CONFERENCE ON CIRCUITS/SYSTEMS, COMPUTERS AND COMMUNICATIONS (ITC-CSCC 2019), 2019, : 72 - 75
  • [43] An improved proportionate least mean p-power algorithm for adaptive filtering
    Xie Zhang
    Siyuan Peng
    Zongze Wu
    Yajing Zhou
    Yuli Fu
    [J]. Signal, Image and Video Processing, 2018, 12 : 59 - 66
  • [44] AN EFFICIENT RECURSIVE TOTAL LEAST-SQUARES ALGORITHM FOR FIR ADAPTIVE FILTERING
    DAVILA, CE
    [J]. IEEE TRANSACTIONS ON SIGNAL PROCESSING, 1994, 42 (02) : 268 - 280
  • [45] New Enhanced Robust Kernel Least Mean Square Adaptive Filtering Algorithm
    Liu, Furong
    Yuan, Wenyi
    Ma, Yongbao
    Zhou, Yi
    Liu, Hongqing
    [J]. PROCEEDINGS OF 2015 INTERNATIONAL CONFERENCE ON ESTIMATION, DETECTION AND INFORMATION FUSION ICEDIF 2015, 2015, : 282 - 285
  • [46] Adaptive weighted constrained least squares algorithm based microphone array robustness beamforming algorithm
    Guo Ye-Cai
    Zhang Ning
    Wu Li-Fu
    Sun Xin-Yu
    [J]. ACTA PHYSICA SINICA, 2015, 64 (17)
  • [47] An improved proportionate least mean p-power algorithm for adaptive filtering
    Zhang, Xie
    Peng, Siyuan
    Wu, Zongze
    Zhou, Yajing
    Fu, Yuli
    [J]. SIGNAL IMAGE AND VIDEO PROCESSING, 2018, 12 (01) : 59 - 66
  • [48] A Logarithmic Total Least Squares Adaptive Filtering Algorithm for Impulsive Noise Suppression
    Zhao Haiquan
    Li Lei
    [J]. JOURNAL OF ELECTRONICS & INFORMATION TECHNOLOGY, 2021, 43 (02) : 284 - 288
  • [49] Spline Adaptive Filtering based on Variable Leaky Least Mean Square Algorithm
    Saenmuang, Adisorn
    Sitjongsataporn, Suchada
    [J]. 2020 8TH INTERNATIONAL ELECTRICAL ENGINEERING CONGRESS (IEECON), 2020,
  • [50] On bias compensated recursive least-squares algorithm for FIR adaptive filtering
    Jia, LJ
    Jin, CZ
    Wada, K
    [J]. ADAPTATION AND LEARNING IN CONTROL AND SIGNAL PROCESSING 2001, 2002, : 347 - 352