Robust Bias-Compensated LMS Algorithm: Design, Performance Analysis and Applications

Fuyi Huang; Fan Song; Sheng Zhang; Hing Cheung So; Jun Yang

doi:10.1109/TVT.2023.3276573

Robust Bias-Compensated LMS Algorithm: Design, Performance Analysis and Applications

Fuyi Huang
, Fan Song^*
, Sheng Zhang
, Hing Cheung So^*
, Jun Yang

^*Corresponding author for this work

Department of Electrical Engineering

Research output: Journal Publications and Reviews › RGC 21 - Publication in refereed journal › peer-review

28 Citations (Scopus)

Abstract

This paper considers the problem of system parameter estimation using adaptive filter. Conventional adaptive algorithms will result in degraded performance in the presence of impulsive noise and biased estimation when the input signal is noisy. To address these issues, this paper proposes a robust bias-compensated least mean squares (R-BC-LMS) algorithm. It is derived by performing the maximum-a-posteriori estimation subject to a constraint on the squared norm of the weight vector difference, and then introducing an unbiasedness criterion to insert a bias compensation term in the update. Under common statistical assumptions, the mean and mean square behaviors of weight deviation are derived for the R-BC-LMS algorithm. In addition, we develop the estimator for the input and output noise variances. Simulations in channel estimation, vehicle handsfree echo cancellation, and direction-of-arrival estimation demonstrate that our method outperforms the competing algorithms.

© 2023 IEEE. Personal use is permitted, but republication/redistribution requires IEEE permission.

Original language	English
Pages (from-to)	13214-13228
Number of pages	15
Journal	IEEE Transactions on Vehicular Technology
Volume	72
Issue number	10
Online published	16 May 2023
DOIs	https://doi.org/10.1109/TVT.2023.3276573
Publication status	Published - Oct 2023

Bibliographical note

Publisher Copyright:
IEEE

Funding

This work was supported in part by the National Natural Science Foundation of China under Grants 62201160, 62261003, 61961004, and 61801401, in part by the Interdisciplinary Scientific Research Foundation of GuangXi University under Grant 2022JCC015, in part by Sichuan Science and Technology Program under Grant 2021YFG0012, and in part by the Fundamental Research Funds for the Central Universities under Grant XJ2021KJZK003.

Research Keywords

Adaptive systems
Bayes methods
Covariance matrices
Direction-of-arrival estimation
direction-of-arrival estimation
Echo cancellation
Estimation
impulsive interferences
least mean squares
Noise measurement
noisy input
robust adaptive signal processing
Signal processing algorithms
spatial spectrum

Access Output

10.1109/TVT.2023.3276573

Other Access Options

Check CityUHK Library

Permanent Link

Right click to copy link

Cite this

@article{535b4ca5d5a442969d0fa7bc1ae183dd,

title = "Robust Bias-Compensated LMS Algorithm: Design, Performance Analysis and Applications",

abstract = "This paper considers the problem of system parameter estimation using adaptive filter. Conventional adaptive algorithms will result in degraded performance in the presence of impulsive noise and biased estimation when the input signal is noisy. To address these issues, this paper proposes a robust bias-compensated least mean squares (R-BC-LMS) algorithm. It is derived by performing the maximum-a-posteriori estimation subject to a constraint on the squared norm of the weight vector difference, and then introducing an unbiasedness criterion to insert a bias compensation term in the update. Under common statistical assumptions, the mean and mean square behaviors of weight deviation are derived for the R-BC-LMS algorithm. In addition, we develop the estimator for the input and output noise variances. Simulations in channel estimation, vehicle handsfree echo cancellation, and direction-of-arrival estimation demonstrate that our method outperforms the competing algorithms. {\textcopyright} 2023 IEEE. Personal use is permitted, but republication/redistribution requires IEEE permission. ",

keywords = "Adaptive systems, Bayes methods, Covariance matrices, Direction-of-arrival estimation, direction-of-arrival estimation, Echo cancellation, Estimation, impulsive interferences, least mean squares, Noise measurement, noisy input, robust adaptive signal processing, Signal processing algorithms, spatial spectrum",

author = "Fuyi Huang and Fan Song and Sheng Zhang and So, \{Hing Cheung\} and Jun Yang",

note = "Publisher Copyright: IEEE",

year = "2023",

month = oct,

doi = "10.1109/TVT.2023.3276573",

language = "English",

volume = "72",

pages = "13214--13228",

journal = "IEEE Transactions on Vehicular Technology",

issn = "0018-9545",

publisher = "IEEE",

number = "10",

}

TY - JOUR

T1 - Robust Bias-Compensated LMS Algorithm

T2 - Design, Performance Analysis and Applications

AU - Huang, Fuyi

AU - Song, Fan

AU - Zhang, Sheng

AU - So, Hing Cheung

AU - Yang, Jun

N1 - Publisher Copyright: IEEE

PY - 2023/10

Y1 - 2023/10

N2 - This paper considers the problem of system parameter estimation using adaptive filter. Conventional adaptive algorithms will result in degraded performance in the presence of impulsive noise and biased estimation when the input signal is noisy. To address these issues, this paper proposes a robust bias-compensated least mean squares (R-BC-LMS) algorithm. It is derived by performing the maximum-a-posteriori estimation subject to a constraint on the squared norm of the weight vector difference, and then introducing an unbiasedness criterion to insert a bias compensation term in the update. Under common statistical assumptions, the mean and mean square behaviors of weight deviation are derived for the R-BC-LMS algorithm. In addition, we develop the estimator for the input and output noise variances. Simulations in channel estimation, vehicle handsfree echo cancellation, and direction-of-arrival estimation demonstrate that our method outperforms the competing algorithms. © 2023 IEEE. Personal use is permitted, but republication/redistribution requires IEEE permission.

AB - This paper considers the problem of system parameter estimation using adaptive filter. Conventional adaptive algorithms will result in degraded performance in the presence of impulsive noise and biased estimation when the input signal is noisy. To address these issues, this paper proposes a robust bias-compensated least mean squares (R-BC-LMS) algorithm. It is derived by performing the maximum-a-posteriori estimation subject to a constraint on the squared norm of the weight vector difference, and then introducing an unbiasedness criterion to insert a bias compensation term in the update. Under common statistical assumptions, the mean and mean square behaviors of weight deviation are derived for the R-BC-LMS algorithm. In addition, we develop the estimator for the input and output noise variances. Simulations in channel estimation, vehicle handsfree echo cancellation, and direction-of-arrival estimation demonstrate that our method outperforms the competing algorithms. © 2023 IEEE. Personal use is permitted, but republication/redistribution requires IEEE permission.

KW - Adaptive systems

KW - Bayes methods

KW - Covariance matrices

KW - Direction-of-arrival estimation

KW - direction-of-arrival estimation

KW - Echo cancellation

KW - Estimation

KW - impulsive interferences

KW - least mean squares

KW - Noise measurement

KW - noisy input

KW - robust adaptive signal processing

KW - Signal processing algorithms

KW - spatial spectrum

UR - https://www.scopus.com/pages/publications/85162865305

UR - https://www.scopus.com/record/pubmetrics.uri?eid=2-s2.0- 85162865305&origin=recordpage

U2 - 10.1109/TVT.2023.3276573

DO - 10.1109/TVT.2023.3276573

M3 - RGC 21 - Publication in refereed journal

AN - SCOPUS:85162865305

SN - 0018-9545

VL - 72

SP - 13214

EP - 13228

JO - IEEE Transactions on Vehicular Technology

JF - IEEE Transactions on Vehicular Technology

IS - 10

ER -

Robust Bias-Compensated LMS Algorithm: Design, Performance Analysis and Applications

Abstract

Bibliographical note

Funding

Research Keywords

Access Output

Other Access Options

Permanent Link

Other Files and Links

Fingerprint

Cite this