Robust multitask diffusion normalized M-estimate subband adaptive filter algorithm over adaptive networks

被引:1
|
作者
Xu, Wenjing
Zhao, Haiquan [1 ]
Lv, Shaohui
机构
[1] Southwest Jiaotong Univ, Key Lab Magnet Suspens Technol & Maglev Vehicle, Minist Educ, Chengdu 610031, Peoples R China
来源
JOURNAL OF THE FRANKLIN INSTITUTE-ENGINEERING AND APPLIED MATHEMATICS | 2023年 / 360卷 / 15期
基金
中国国家自然科学基金;
关键词
IMPULSIVE NOISE;
D O I
10.1016/j.jfranklin.2023.08.025
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
In recent years, the multitask diffusion least mean square (MD-LMS) algorithm has been extensively applied in distributed parameter estimation and target tracking of multitask network. However, its performance degrades under colored input signals or impulsive noises. To overcome these two drawbacks, this paper introduces the subband adaptive filter (SAF) into a multitask network for the first time, and a robust multitask diffusion normalized M-estimate subband adaptive filtering (MD-NMSAF) algorithm is proposed, which solves the global network optimization problem based on the modified Huber (MH) function in a distributed manner, achieves robustness to impulsive noise, and significantly improves the convergence performance of the MD-LMS algorithm. Compared with existing robust multitask diffusion affine projection algorithms (MD-APAs), the computational complexity of the proposed MD-NMSAF algorithm is greatly reduced. In addition, we analyze the mean and mean-square stability conditions of MD-NMSAF, provide theoretical models characterizing the network mean square deviation (MSD) behaviors of transient and steady-state, and further verify their correctness by computer simulations. Simulation results under different network topologies, input signals, filter lengths and impulsive noise types fully demonstrate the performance advantages of the proposed MD-NMSAF algorithm over its competitors in terms of steady-state estimation accuracy and convergence speed.(c) 2023 The Franklin Institute. Published by Elsevier Inc. All rights reserved.
引用
收藏
页码:11197 / 11219
页数:23
相关论文
共 50 条
  • [41] Robust Widely Linear Affine Projection M-Estimate Adaptive Algorithm: Performance Analysis and Application
    Lv, Shaohui
    Zhao, Haiquan
    Xu, Wenjing
    IEEE TRANSACTIONS ON SIGNAL PROCESSING, 2023, 71 : 3623 - 3636
  • [42] ADAPTIVE REGULARIZED DIFFUSION ADAPTATION OVER MULTITASK NETWORKS
    Monajemi, Sadaf
    Sanei, Saeid
    Ong, Sim-Heng
    Sayed, Ali H.
    2015 IEEE INTERNATIONAL WORKSHOP ON MACHINE LEARNING FOR SIGNAL PROCESSING, 2015,
  • [43] Pipelined robust M-estimate adaptive second-order Volterra filter against impulsive noise
    Zhang, Jiashu
    Pang, Yanjie
    DIGITAL SIGNAL PROCESSING, 2014, 26 : 71 - 80
  • [44] ROBUST FREQUENCY-DOMAIN RECURSIVE LEAST M-ESTIMATE ADAPTIVE FILTER FOR ACOUSTIC SYSTEM IDENTIFICATION
    He, Hongsen
    Chen, Jingdong
    Benesty, Jacob
    Yu, Yi
    2020 IEEE INTERNATIONAL CONFERENCE ON ACOUSTICS, SPEECH, AND SIGNAL PROCESSING, 2020, : 471 - 475
  • [45] ROBUST RECURSIVE LEAST M-ESTIMATE ADAPTIVE FILTER FOR THE IDENTIFICATION OF LOW-RANK ACOUSTIC SYSTEMS
    He, Hongsen
    Chen, Jingdong
    Benesty, Jacob
    Yu, Yi
    2021 IEEE INTERNATIONAL CONFERENCE ON ACOUSTICS, SPEECH AND SIGNAL PROCESSING (ICASSP 2021), 2021, : 940 - 944
  • [46] Total Least Squares Normalized Subband Adaptive Filter Algorithm for Noisy Input
    Zhao, Haiquan
    Chen, Yida
    Liu, Jun
    Zhu, Yingying
    IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS II-EXPRESS BRIEFS, 2022, 69 (03) : 1977 - 1981
  • [47] A New Normalized Subband Adaptive Filter Algorithm with Individual Variable Step Sizes
    Yu, Yi
    Zhao, Haiquan
    Chen, Badong
    CIRCUITS SYSTEMS AND SIGNAL PROCESSING, 2016, 35 (04) : 1407 - 1418
  • [48] Polynomial Constraint Generalized Maximum Correntropy Normalized Subband Adaptive Filter Algorithm
    Liu, Dongxu
    Zhao, Haiquan
    He, Xiaoqiong
    Zhou, Lijun
    CIRCUITS SYSTEMS AND SIGNAL PROCESSING, 2022, 41 (04) : 2379 - 2396
  • [49] Polynomial Constraint Generalized Maximum Correntropy Normalized Subband Adaptive Filter Algorithm
    Dongxu Liu
    Haiquan Zhao
    Xiaoqiong He
    Lijun Zhou
    Circuits, Systems, and Signal Processing, 2022, 41 : 2379 - 2396
  • [50] A New Normalized Subband Adaptive Filter Algorithm with Individual Variable Step Sizes
    Yi Yu
    Haiquan Zhao
    Badong Chen
    Circuits, Systems, and Signal Processing, 2016, 35 : 1407 - 1418