Structured Bayesian Super-Resolution Forward-Looking Imaging for Maneuvering Platforms Based on Enhanced Sparsity Model
Sparse reconstruction-based imaging techniques can be utilized to solve forward-looking imaging problems with limited azimuth resolution. However, these methods perform well only under the traditional model for the platform with low speed, and the performance deteriorates for the maneuvering traject...
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| Format: | Article |
| Language: | English |
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MDPI AG
2025-02-01
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| Series: | Remote Sensing |
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| Online Access: | https://www.mdpi.com/2072-4292/17/5/775 |
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| author | Yiheng Guo Yujie Liang Yi Liang Xiangwei Sun |
| author_facet | Yiheng Guo Yujie Liang Yi Liang Xiangwei Sun |
| author_sort | Yiheng Guo |
| collection | DOAJ |
| description | Sparse reconstruction-based imaging techniques can be utilized to solve forward-looking imaging problems with limited azimuth resolution. However, these methods perform well only under the traditional model for the platform with low speed, and the performance deteriorates for the maneuvering trajectory. In this paper, a structured Bayesian super-resolution forward-looking imaging algorithm for maneuvering platforms under an enhanced sparsity model is proposed. An enhanced sparsity model for maneuvering platforms is established to address the reconstruction problem, and a hierarchical Student-t (ST) prior is designed to model the distribution characteristics of the sparse imaging scene. To further leverage prior information about structural characteristics of the scatterings, coupled patterns among neighboring pixels are incorporated to construct a structured sparse prior. Finally, forward-looking imaging parameters are estimated using the expectation/maximization-based variational Bayesian inference. Numerical simulations validate the effectiveness of the proposed algorithm and the superiority over conventional methods based on pixel sparse assumptions in forward-looking scenes for maneuvering platforms. |
| format | Article |
| id | doaj-art-2b3e3f0bc1e2430596d3ed719e121c7a |
| institution | OA Journals |
| issn | 2072-4292 |
| language | English |
| publishDate | 2025-02-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj-art-2b3e3f0bc1e2430596d3ed719e121c7a2025-08-20T02:06:13ZengMDPI AGRemote Sensing2072-42922025-02-0117577510.3390/rs17050775Structured Bayesian Super-Resolution Forward-Looking Imaging for Maneuvering Platforms Based on Enhanced Sparsity ModelYiheng Guo0Yujie Liang1Yi Liang2Xiangwei Sun3National Key Laboratory of Radar Signal Processing, Xidian University, Xi’an 710071, ChinaSchool of Optoelectronic Engineering, Xidian University, Xi’an 710071, ChinaNational Key Laboratory of Radar Signal Processing, Xidian University, Xi’an 710071, ChinaXi’an LeiTong Science &Technology Co., Ltd., Xi’an 710100, ChinaSparse reconstruction-based imaging techniques can be utilized to solve forward-looking imaging problems with limited azimuth resolution. However, these methods perform well only under the traditional model for the platform with low speed, and the performance deteriorates for the maneuvering trajectory. In this paper, a structured Bayesian super-resolution forward-looking imaging algorithm for maneuvering platforms under an enhanced sparsity model is proposed. An enhanced sparsity model for maneuvering platforms is established to address the reconstruction problem, and a hierarchical Student-t (ST) prior is designed to model the distribution characteristics of the sparse imaging scene. To further leverage prior information about structural characteristics of the scatterings, coupled patterns among neighboring pixels are incorporated to construct a structured sparse prior. Finally, forward-looking imaging parameters are estimated using the expectation/maximization-based variational Bayesian inference. Numerical simulations validate the effectiveness of the proposed algorithm and the superiority over conventional methods based on pixel sparse assumptions in forward-looking scenes for maneuvering platforms.https://www.mdpi.com/2072-4292/17/5/775structured Bayesian super-resolutionmaneuvering platformsenhanced sparsity modelhierarchical ST priorvariational Bayesian inference |
| spellingShingle | Yiheng Guo Yujie Liang Yi Liang Xiangwei Sun Structured Bayesian Super-Resolution Forward-Looking Imaging for Maneuvering Platforms Based on Enhanced Sparsity Model Remote Sensing structured Bayesian super-resolution maneuvering platforms enhanced sparsity model hierarchical ST prior variational Bayesian inference |
| title | Structured Bayesian Super-Resolution Forward-Looking Imaging for Maneuvering Platforms Based on Enhanced Sparsity Model |
| title_full | Structured Bayesian Super-Resolution Forward-Looking Imaging for Maneuvering Platforms Based on Enhanced Sparsity Model |
| title_fullStr | Structured Bayesian Super-Resolution Forward-Looking Imaging for Maneuvering Platforms Based on Enhanced Sparsity Model |
| title_full_unstemmed | Structured Bayesian Super-Resolution Forward-Looking Imaging for Maneuvering Platforms Based on Enhanced Sparsity Model |
| title_short | Structured Bayesian Super-Resolution Forward-Looking Imaging for Maneuvering Platforms Based on Enhanced Sparsity Model |
| title_sort | structured bayesian super resolution forward looking imaging for maneuvering platforms based on enhanced sparsity model |
| topic | structured Bayesian super-resolution maneuvering platforms enhanced sparsity model hierarchical ST prior variational Bayesian inference |
| url | https://www.mdpi.com/2072-4292/17/5/775 |
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