22 November 2021 Low rank and sparse matrix decomposition based multiple targets extraction for forward-looking scanning radar
Wenchao Li, Wentao Zhang, Shirui Yang, Yulin Huang, Jianyu Yang
Author Affiliations +
Abstract

Forward-looking scanning radar is capable of obtaining the real beam image of terrain in front of the flight platform, and can be used in military and civilian fields, such as sea surface surveillance, search and rescue, etc. However, it is difficult to extract multiple targets from the real beam image due to its poor azimuth resolution. A multiple-target extraction scheme based on low-rank and sparse matrix decomposition is proposed for forward-looking scanning radar. In the scheme, an image with good azimuth resolution is obtained first by the deconvolution preprocessing, and then it is used to map a patch-image. Second, using the low-rank property of the patch-image and the sparse property of the targets of interest, the target extraction is converted into an optimization problem of low-rank and sparse matrix decomposition. Third, the extraction results are obtained by solving this optimization problem using the alternating direction method of multipliers. Finally, simulation and experiment results are given to verify the effectiveness of the proposed scheme.

© 2021 Society of Photo-Optical Instrumentation Engineers (SPIE) 1931-3195/2021/$28.00 © 2021 SPIE
Wenchao Li, Wentao Zhang, Shirui Yang, Yulin Huang, and Jianyu Yang "Low rank and sparse matrix decomposition based multiple targets extraction for forward-looking scanning radar," Journal of Applied Remote Sensing 15(4), 046504 (22 November 2021). https://doi.org/10.1117/1.JRS.15.046504
Received: 2 July 2021; Accepted: 8 November 2021; Published: 22 November 2021
Lens.org Logo
CITATIONS
Cited by 2 scholarly publications.
Advertisement
Advertisement
RIGHTS & PERMISSIONS
Get copyright permission  Get copyright permission on Copyright Marketplace
KEYWORDS
Super resolution

Radar

Signal to noise ratio

Antennas

Deconvolution

Detection and tracking algorithms

Image resolution

Back to Top