We found a match
Your institution may have access to this item. Find your institution then sign in to continue.
- Title
Inverse Synthetic Aperture Radar Imaging Based on the Non-Convex Regularization Model.
- Authors
Yanan ZHAO; Fengyuan YANG; Chao WANG; Fangjie YE; Feng ZHU; Yu LIU
- Abstract
Compressed 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 Lp (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 Lp 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
- Subjects
INVERSE synthetic aperture radar; SYNTHETIC apertures; COMPRESSED sensing
- Publication
Radioengineering, 2024, Vol 33, Issue 1, p54
- ISSN
1210-2512
- Publication type
Article
- DOI
10.13164/re.2024.0054