Super-resolution ISAR Imaging using Off-the-grid Structured Low-rank Method
| Author(s) | Zhang, Bangjie | |
| Author(s) | Xu, Gang | |
| Author(s) | Xia, Xiang-Gen | |
| Author(s) | Yu, Hanwen | |
| Author(s) | Xing, Mengdao | |
| Author(s) | Hong, Wei | |
| Date Accessioned | 2024-12-04T20:31:06Z | |
| Date Available | 2024-12-04T20:31:06Z | |
| Publication Date | 2024-11-12 | |
| Description | This article was originally published in IEEE Transactions on Antennas and Propagation. The version of record is available at: https://doi.org/10.1109/TAP.2024.3492503. © 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. This article will be embargoed until 11/12/2026. | |
| Abstract | Inverse synthetic aperture radar (ISAR) imaging relies on wideband waveform and viewing angle variation to achieve range and cross-range resolutions, respectively. To enhance the resolutions of two-dimensional (2-D) image, sparse signal processing techniques, such as compressed sensing (CS), have been applied to ISAR imaging using a sparse prior. Despite its efficiency in super-resolution imaging, the performance of CS is constrained due to the mismatch of discrete dictionary, such as Fourier transform. To address this issue, we propose a novel off-the-grid super-resolution ISAR imaging algorithm that employs a structured low-rank approach to effectively extrapolate the data bandwidth and aperture. To fully capture the low-rank property of ISAR data, the structured data model is constructed and its low-rank property is deduced to exhibit that the signal is embedded in a limited dimensional subspace. Then, the annihilating filter is derived by constructing structured data matrix to formulate the proposed structured low-rank method, termed as Off-the-grid Super-resolution using Annihilation Constraint (OSAC). Taking into account that the super-resolution imaging is highly reliant on the accuracy of annihilating filter, the optimal annihilating filter is also estimated with the updating of extrapolated ISAR data. Through iterative updates of the annihilating filter and solution of the minimization problem, the super-resolution ISAR imaging can be achieved by avoiding the discrete mismatch of conventional CS method. Due to the effective exploration of structured low-rank property, the proposed OSAC algorithm offers superior precision in scatterer location and structure interpretation of a target. Experimental results using both simulated and real data are presented to verify the enhanced performance of 2-D resolution in ISAR imaging. | |
| Sponsor | This work was supported by the National Science Foundation of China (NSFC) under Grant 62071113, in part by the Natural Science Foundation of Jiangsu Province under Grant BK20211559, in part by the Fundamental Research Funds for the Central Universities under Grant 2242022k60008 and Grant 2242022R40008. | |
| Citation | B. Zhang, G. Xu, X. -G. Xia, H. Yu, M. Xing and W. Hong, "Super-resolution ISAR Imaging using Off-the-grid Structured Low-rank Method," in IEEE Transactions on Antennas and Propagation, doi: 10.1109/TAP.2024.3492503. | |
| ISSN | 1558-2221 | |
| URL | https://udspace.udel.edu/handle/19716/35635 | |
| Language | en_US | |
| Publisher | IEEE Transactions on Antennas and Propagation | |
| Keywords | inverse synthetic aperture radar (ISAR) | |
| Keywords | super-resolution | |
| Keywords | off-the-grid compressed sensing (CS) | |
| Keywords | annihilating filter | |
| Title | Super-resolution ISAR Imaging using Off-the-grid Structured Low-rank Method | |
| Type | Article |
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