Sparse SAR Imaging Method for Ground Moving Target via GMTSI-Net

• Published in: Remote sensing (Basel, Switzerland) Vol. 14; no. 17; p. 4404
• Main Authors: Chen, Luwei, Ni, Jiacheng, Luo, Ying, He, Qifang, Lu, Xiaofei
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01-09-2022
• Subjects: Adaptability; Algorithms; Azimuth; GMT sparse imaging network (GMTSI-Net); ground moving targets (GMT); Iterative methods; iterative soft threshold algorithm (ISTA); Iterative solution; Matched filters; Methods; Moving targets; Optimization; Parameter estimation; Radar; Radar imaging; Sampling; Signal to noise ratio; sparse imaging; Sparsity; Synthetic aperture radar; synthetic aperture radar (SAR); Time series; Velocity
• ISSN: 2072-4292; 2072-4292
• DOI: 10.3390/rs14174404

Ground moving targets (GMT), due to the existence of velocity in range and azimuth direction, will lead to the deviation from their true position and defocus in the azimuth direction during the synthetic aperture radar (SAR) imaging process. To address this problem and compress the amount of echo data, a sparse SAR imaging method for ground moving targets is proposed. Specifically, we first constructed a two-dimensional sparse observation model of the GMT based on matched filter operators. Then, the observation model was solved by a deep network, GMT sparse imaging network (GMTSI-Net), which was mainly obtained by unfolding an iterative soft threshold algorithm (ISTA)-based iterative solution. Furthermore, we designed an adaptive unfolding module in the imaging network to improve the adaptability of the network to the input of echo data with different sampling ratios. The proposed imaging network can achieve faster and more accurate SAR images of ground moving targets under a low sampling ratio and signal-to-noise ratio (SNR). Simulated and measured data experiments were conducted to demonstrate the performance of imaging quality of the proposed method.