Multi-task Learning of Sparse Autofocusing for High-Resolution SAR Imagery

Lei YANG; Su ZHANG; Bo HUANG; Minghui GAI; Pucheng LI

doi:10.11999/JEIT200300

Volume 43 Issue 9

Sep. 2021

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Article Navigation > Journal of Electronics & Information Technology > 2021 > 43(9): 2711-2719

Lei YANG, Su ZHANG, Bo HUANG, Minghui GAI, Pucheng LI. Multi-task Learning of Sparse Autofocusing for High-Resolution SAR Imagery[J]. Journal of Electronics & Information Technology, 2021, 43(9): 2711-2719. doi: 10.11999/JEIT200300

Citation:

Lei YANG, Su ZHANG, Bo HUANG, Minghui GAI, Pucheng LI. Multi-task Learning of Sparse Autofocusing for High-Resolution SAR Imagery[J]. Journal of Electronics & Information Technology, 2021, 43(9): 2711-2719. doi: 10.11999/JEIT200300

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Multi-task Learning of Sparse Autofocusing for High-Resolution SAR Imagery

doi: 10.11999/JEIT200300

1.
Tianjin Key Laboratory for Advanced Signal Processing, Civil Aviation University of China, Tianjin 300300, China
2.
Institute of Electronic Engineering, Chinese Academy of Engineering Physics, Mianyang 621999, China

Funds: The National Natural Science Foundation of China(61601470), The Natural Science Foundation of Tianjin, China (16JCYBJC41200), The Equipment Pre-research Fund(61406190101)

Received Date: 2020-04-24
Rev Recd Date: 2021-02-28

Available Online: 2021-03-22

Publish Date: 2021-09-16

Abstract

Abstract

As it is difficult to balance the sparse and focusing features for conventional sparse autofocusing algorithm of Synthetic Aperture Radar (SAR), a Multi-task Learning Sparse Autofocusing (MtL-SA) algorithm is proposed under a novel Alternating Direction Method of Multipliers (ADMM) in this paper. The image entropy norm is introduced to model the focusing feature of the SAR imagery, and it is minimized in a regularized manner using the proximal algorithm. To overcome the non-convexity of the original objective function, a surrogate function under the ADMM framework is designed and optimized accordingly. This ensures closed-form solution of the errors and the focusing feature. Besides, the $ \ell {_1}$-norm is applied to denote the intended sparse feature of the SAR imagery, and a complex-valued proximity operator is derived for the range-compressed SAR data. Due to the cooperative framework, both the features can be solved and achieved with high robustness and acceptable accuracy. Compared with conventions, the computational efficiency improved twice orders in terms of CPU time. The proposed MtL-SA algorithm can realize the analytical solutions of the sparse and focusing features, so as to improve the robustness of the joint enhancement. Experiments using airborne simulated and raw SAR data are performed to verify the effectiveness of the proposed algorithm. Phase transition analysis is applied to examine the superiority of the proposed algorithm compared with the conventions in terms of both quantitative and qualitative.
- Synthetic Aperture Radar (SAR),
- Multi-task learning,
- Multi-feature enhancement,
- Entropy norm,
- Proximity operator

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References(20)

References

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