Inverse Synthetic Aperture Radar Imaging Based on the Non-Convex Regularization Model

Simple item page
dc.contributor.authorZhao, Y.
dc.contributor.authorYang, F.
dc.contributor.authorWang, C.
dc.contributor.authorYe, F.
dc.contributor.authorZhu, F.
dc.contributor.authorLiu, Y.
dc.coverage.issue1cs
dc.coverage.volume33cs
dc.date.accessioned2024-05-28T12:43:34Z
dc.date.available2024-05-28T12:43:34Z
dc.date.issued2024-04cs
dc.description.abstractCompressed Sensing (CS) has been shown to be an effective technique for improving the resolution of inverse synthetic aperture radar (ISAR) imaging and reducing the hardware requirements of radar systems. In this paper, our focus is on the l_p 0 p 1 model, which is a well-known non-convex and non-Lipschitz regularization model in the field of compressed sensing. In this study, we propose a novel algorithm, namely the Accelerated Iterative Support Shrinking with Full Linearization (AISSFL) algorithm, which aims to solve the l_p regularization model for ISAR imaging. The AISSFL algorithm draws inspiration from the Majorization-Minimization (MM) iteration algorithm and integrates the principles of support shrinkage and Nestrove's acceleration technique. The algorithm employed in this study demonstrates simplicity and efficiency. Numerical experiments demonstrate that AISSFL performs well in the field of ISAR imaging.en
dc.formattextcs
dc.format.extent54-61cs
dc.format.mimetypeapplication/pdfen
dc.identifier.citationRadioengineering. 2024 vol. 33, iss. 1, s. 54-61. ISSN 1210-2512cs
dc.identifier.doi10.13164/re.2024.0054en
dc.identifier.issn1210-2512
dc.identifier.urihttps://hdl.handle.net/11012/245666
dc.language.isoencs
dc.publisherSpolečnost pro radioelektronické inženýrstvícs
dc.relation.ispartofRadioengineeringcs
dc.relation.urihttps://www.radioeng.cz/fulltexts/2024/24_01_0054_0061.pdfcs
dc.rightsCreative Commons Attribution 4.0 International licenseen
dc.rights.accessopenAccessen
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/en
dc.subjectISARen
dc.subjectcompressed sensingen
dc.subjectnon-convex optimizationen
dc.subjectAISSFL algorithmen
dc.titleInverse Synthetic Aperture Radar Imaging Based on the Non-Convex Regularization Modelen
dc.type.driverarticleen
dc.type.statusPeer-revieweden
dc.type.versionpublishedVersionen
eprints.affiliatedInstitution.facultyFakulta eletrotechniky a komunikačních technologiícs
Files
Original bundle
Now showing 1 - 1 of 1
Thumbnail Image
Name:
24_01_0054_0061.pdf
Size:
490.65 KB
Format:
Adobe Portable Document Format
Description:
Download
Collections
2024/1