Hybrid Ordinary-Welsch Function Based Robust Matrix Completion for MIMO radar

Hao Nan Sheng; Zhi-yong Wang; Zhaofeng Liu; Hing Cheung So

doi:10.1109/TAES.2024.3494766

Hybrid Ordinary-Welsch Function Based Robust Matrix Completion for MIMO radar

Hao Nan Sheng, Zhi-yong Wang^*, Zhaofeng Liu, Hing Cheung So

^*Corresponding author for this work

Department of Electrical Engineering

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

30 Downloads (CityUHK Scholars)

Abstract

In this paper, we consider a sub-Nyquist sampled multiple-input multiple-output (MIMO) radar scenario where the observations are contaminated by impulsive non-Gaussian clutter, which introduces outliers. To recover the missing data, we propose a robust matrix completion (MC) method with a regularizer that acts on outliers. This regularizer whose solution is unbiased, sparse and continuous, is generated by the hybrid ordinary-Welsch (HOW) function, aiming to classify each measurement as normal, semi-contaminated or contaminated, and then handle it appropriately. Then proximal block coordinate descent (BCD) is leveraged to tackle the HOW-based MC problem and the convergence property and computational cost of the developed algorithm are analyzed. Experimental results validate the superior performance of our method compared to existing approaches in terms of MC and direction-of-arrival estimation accuracies as well as runtime in the presence of Gaussian mixture noise and K-distributed clutter. Our codes are available at https://github.com/ShuDun23/robust-MIMO-MC. © 2024 IEEE

Original language	English
Pages (from-to)	3950 - 3962
Journal	IEEE Transactions on Aerospace and Electronic Systems
Volume	61
Issue number	2
Online published	8 Nov 2024
DOIs	https://doi.org/10.1109/TAES.2024.3494766
Publication status	Published - Apr 2025

Funding

The work described in this paper was supported by the Research Grant of Shenzhen Research Institute, City University of Hong Kong, Shenzhen, China [Project No. R-IND25501], and the Research Grants Council of the Hong Kong Special Administrative Region, China [Project No. CityU 11207922].

Research Keywords

MIMO radar
outlier
robust low-rank matrix completion
target localization
Welsch function

Publisher's Copyright Statement

COPYRIGHT TERMS OF DEPOSITED POSTPRINT FILE: © 2024 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. Sheng, H. N., Wang, Z., Liu, Z., & So, H. C. (2024). Hybrid Ordinary-Welsch Function Based Robust Matrix Completion for MIMO radar. IEEE Transactions on Aerospace and Electronic Systems. Advance online publication. https://doi.org/10.1109/TAES.2024.3494766

Access Output

10.1109/TAES.2024.3494766

253576862.pdfPost-print, 2.79 MB

Other Access Options

Check CityUHK Library

Permanent Link

Right click to copy link

Cite this

@article{4b2363d962b543d7ab7e46d973f78648,

title = "Hybrid Ordinary-Welsch Function Based Robust Matrix Completion for MIMO radar",

abstract = "In this paper, we consider a sub-Nyquist sampled multiple-input multiple-output (MIMO) radar scenario where the observations are contaminated by impulsive non-Gaussian clutter, which introduces outliers. To recover the missing data, we propose a robust matrix completion (MC) method with a regularizer that acts on outliers. This regularizer whose solution is unbiased, sparse and continuous, is generated by the hybrid ordinary-Welsch (HOW) function, aiming to classify each measurement as normal, semi-contaminated or contaminated, and then handle it appropriately. Then proximal block coordinate descent (BCD) is leveraged to tackle the HOW-based MC problem and the convergence property and computational cost of the developed algorithm are analyzed. Experimental results validate the superior performance of our method compared to existing approaches in terms of MC and direction-of-arrival estimation accuracies as well as runtime in the presence of Gaussian mixture noise and K-distributed clutter. Our codes are available at https://github.com/ShuDun23/robust-MIMO-MC. {\textcopyright} 2024 IEEE",

keywords = "MIMO radar, outlier, robust low-rank matrix completion, target localization, Welsch function",

author = "Sheng, {Hao Nan} and Zhi-yong Wang and Zhaofeng Liu and So, {Hing Cheung}",

year = "2025",

month = apr,

doi = "10.1109/TAES.2024.3494766",

language = "English",

volume = "61",

pages = "3950 -- 3962",

journal = "IEEE Transactions on Aerospace and Electronic Systems",

issn = "0018-9251",

publisher = "IEEE",

number = "2",

}

TY - JOUR

T1 - Hybrid Ordinary-Welsch Function Based Robust Matrix Completion for MIMO radar

AU - Sheng, Hao Nan

AU - Wang, Zhi-yong

AU - Liu, Zhaofeng

AU - So, Hing Cheung

PY - 2025/4

Y1 - 2025/4

N2 - In this paper, we consider a sub-Nyquist sampled multiple-input multiple-output (MIMO) radar scenario where the observations are contaminated by impulsive non-Gaussian clutter, which introduces outliers. To recover the missing data, we propose a robust matrix completion (MC) method with a regularizer that acts on outliers. This regularizer whose solution is unbiased, sparse and continuous, is generated by the hybrid ordinary-Welsch (HOW) function, aiming to classify each measurement as normal, semi-contaminated or contaminated, and then handle it appropriately. Then proximal block coordinate descent (BCD) is leveraged to tackle the HOW-based MC problem and the convergence property and computational cost of the developed algorithm are analyzed. Experimental results validate the superior performance of our method compared to existing approaches in terms of MC and direction-of-arrival estimation accuracies as well as runtime in the presence of Gaussian mixture noise and K-distributed clutter. Our codes are available at https://github.com/ShuDun23/robust-MIMO-MC. © 2024 IEEE

AB - In this paper, we consider a sub-Nyquist sampled multiple-input multiple-output (MIMO) radar scenario where the observations are contaminated by impulsive non-Gaussian clutter, which introduces outliers. To recover the missing data, we propose a robust matrix completion (MC) method with a regularizer that acts on outliers. This regularizer whose solution is unbiased, sparse and continuous, is generated by the hybrid ordinary-Welsch (HOW) function, aiming to classify each measurement as normal, semi-contaminated or contaminated, and then handle it appropriately. Then proximal block coordinate descent (BCD) is leveraged to tackle the HOW-based MC problem and the convergence property and computational cost of the developed algorithm are analyzed. Experimental results validate the superior performance of our method compared to existing approaches in terms of MC and direction-of-arrival estimation accuracies as well as runtime in the presence of Gaussian mixture noise and K-distributed clutter. Our codes are available at https://github.com/ShuDun23/robust-MIMO-MC. © 2024 IEEE

KW - MIMO radar

KW - outlier

KW - robust low-rank matrix completion

KW - target localization

KW - Welsch function

UR - http://www.scopus.com/inward/record.url?scp=85209758709&partnerID=8YFLogxK

UR - https://www.scopus.com/record/pubmetrics.uri?eid=2-s2.0-85209758709&origin=recordpage

U2 - 10.1109/TAES.2024.3494766

DO - 10.1109/TAES.2024.3494766

M3 - RGC 21 - Publication in refereed journal

SN - 0018-9251

VL - 61

SP - 3950

EP - 3962

JO - IEEE Transactions on Aerospace and Electronic Systems

JF - IEEE Transactions on Aerospace and Electronic Systems

IS - 2

ER -

Hybrid Ordinary-Welsch Function Based Robust Matrix Completion for MIMO radar

Abstract

Funding

Research Keywords

Publisher's Copyright Statement

Access Output

Other Access Options

Permanent Link

Other Files and Links

Fingerprint

GRF: Advanced Factorization Approaches for Low-Rank Matrix Recovery

Cite this

Hybrid Ordinary-Welsch Function Based Robust Matrix Completion for MIMO radar

Abstract

Funding

Research Keywords

Publisher's Copyright Statement

Access Output

Other Access Options

Permanent Link

Other Files and Links

Fingerprint

Projects

GRF: Advanced Factorization Approaches for Low-Rank Matrix Recovery

Cite this