L1-norm regularization-based sparse SAR autofocusing imaging: initial result

Xingmeng Lu; Jingjing Zhang; Yufan Song; Zehao Liu; Hui Bi

doi:10.1117/12.2653470

27 September 2022 L₁-norm regularization-based sparse SAR autofocusing imaging: initial result

Xingmeng Lu, Jingjing Zhang, Yufan Song, Zehao Liu, Hui Bi

Proceedings Volume 12346, 2nd International Conference on Information Technology and Intelligent Control (CITIC 2022); 123460N (2022) https://doi.org/10.1117/12.2653470
Event: 2nd International Conference on Information Technology and Intelligent Control (CITIC 2022), 2022, Kunming, China

Abstract

The uncertainty of SAR platform position or non-ideal propagation mediums in synthetic aperture radar (SAR) imaging will cause phase error, which can result in image defocusing. To solve this problem, autofocusing technology in SAR imaging is used to correct the unknown phase error based on the collected data, and hence obtain the well focused image. In this paper, we propose an L₁-norm regularization based sparse SAR autofocusing imaging method. The method uses an iterative algorithm to implement both phase error estimation and L₁ regularization reconstruction. Firstly, the proposed method introduces the phase error term into SAR imaging observation model based on the autofocusing principle. Then it will estimate and correct phase error during the sparse imaging by solving an L₁-norm regularization problem. And thus obtain well-focused sparse SAR images. Experimental results based on real data verify the effectiveness of the proposed method.

Citation Download Citation

Xingmeng Lu, Jingjing Zhang, Yufan Song, Zehao Liu, and Hui Bi "L₁-norm regularization-based sparse SAR autofocusing imaging: initial result", Proc. SPIE 12346, 2nd International Conference on Information Technology and Intelligent Control (CITIC 2022), 123460N (27 September 2022); https://doi.org/10.1117/12.2653470

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KEYWORDS

Synthetic aperture radar

Error analysis

Image restoration

Radar imaging

Reconstruction algorithms

Signal processing

Unmanned aerial vehicles

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