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Remote Sensing
Volume 17
Issue 3
10.3390/rs17030477
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This is an early access version, the complete PDF, HTML, and XML versions will be available soon.
Article

A Robust Direction-of-Arrival (DOA) Estimator for Weak Targets Based on a Dimension-Reduced Matrix Filter with Deep Nulling and Multiple-Measurement-Vector Orthogonal Matching Pursuit

by
Shoudong Wang
,
Haozhong Wang
*,
Zhaoxiang Bian
,
Susu Chen
,
Penghua Song
,
Bolin Su
and
Wei Gao
School of Marine Technology, Ocean University of China, Qingdao 266100, China
*
Author to whom correspondence should be addressed.
Remote Sens. 2025, 17(3), 477; https://doi.org/10.3390/rs17030477
Submission received: 3 December 2024 / Revised: 24 January 2025 / Accepted: 27 January 2025 / Published: 30 January 2025
(This article belongs to the Special Issue Ocean Remote Sensing Based on Radar, Sonar and Optical Techniques)
Versions Notes

Abstract

In the field of target localization, improving direction-of-arrival (DOA) estimation methods for weak targets in the context of strong interference remains a significant challenge. This paper presents a robust DOA estimator for localizing weak signals of interest in an environment with strong interfering sources that improve passive sonar DOA estimation. The presented estimator combines a multiple-measurement-vector orthogonal matching pursuit (MOMP) algorithm and a dimension-reduced matrix filter with deep nulling (DR-MFDN). Strong interfering sources are adaptively suppressed by employing the DR-MFDN, and the beam-space passband robustness is improved. In addition, Gaussian pre-whitening is used to prevent noise colorization. Simulations and experimental results demonstrate that the presented estimator outperforms a conventional estimator based on a dimension-reduced matrix filter with nulling (DR-MFN) and the multiple signal classification algorithm in terms of interference suppression and localization accuracy. Moreover, the presented estimator can effectively handle short snapshots, and it exhibits superior resolution by considering the signal sparsity.
Keywords: matrix filter; DOA estimation; weak target; pre-whitening operation; sparse representation matrix filter; DOA estimation; weak target; pre-whitening operation; sparse representation

Share and Cite

MDPI and ACS Style

Wang, S.; Wang, H.; Bian, Z.; Chen, S.; Song, P.; Su, B.; Gao, W. A Robust Direction-of-Arrival (DOA) Estimator for Weak Targets Based on a Dimension-Reduced Matrix Filter with Deep Nulling and Multiple-Measurement-Vector Orthogonal Matching Pursuit. Remote Sens. 2025, 17, 477. https://doi.org/10.3390/rs17030477

AMA Style

Wang S, Wang H, Bian Z, Chen S, Song P, Su B, Gao W. A Robust Direction-of-Arrival (DOA) Estimator for Weak Targets Based on a Dimension-Reduced Matrix Filter with Deep Nulling and Multiple-Measurement-Vector Orthogonal Matching Pursuit. Remote Sensing. 2025; 17(3):477. https://doi.org/10.3390/rs17030477

Chicago/Turabian Style

Wang, Shoudong, Haozhong Wang, Zhaoxiang Bian, Susu Chen, Penghua Song, Bolin Su, and Wei Gao. 2025. "A Robust Direction-of-Arrival (DOA) Estimator for Weak Targets Based on a Dimension-Reduced Matrix Filter with Deep Nulling and Multiple-Measurement-Vector Orthogonal Matching Pursuit" Remote Sensing 17, no. 3: 477. https://doi.org/10.3390/rs17030477

APA Style

Wang, S., Wang, H., Bian, Z., Chen, S., Song, P., Su, B., & Gao, W. (2025). A Robust Direction-of-Arrival (DOA) Estimator for Weak Targets Based on a Dimension-Reduced Matrix Filter with Deep Nulling and Multiple-Measurement-Vector Orthogonal Matching Pursuit. Remote Sensing, 17(3), 477. https://doi.org/10.3390/rs17030477

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