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

Abstract

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.

A Multicomponent Linear Frequency Modulation Signal-Separation Network for Multi-Moving-Target Imaging in the SAR-Ground-Moving-Target Indication System

“…Notably, when the SCNR exceeded 0 dB, MLFMSS-Net achieved a correlation coefficient exceeding 95%. This is because MLFMSS-Net was trained by minimizing the squared Euclidean distance, i.e., min ∥ f ϕ (I) − O∥ 2 2 . Once this training process was complete, the optimal parameters ϕ could be obtained.…”

“…The number of iterations is updated by k ← k + 1. The aforementioned steps are repeated to extract a new LFM signal component from the residual rs k (n) until k either exceeds the maximum number K max of moving targets or the average power of the residual ∥rs k (n)∥ 2 2 is less than an empirical threshold value ε. Given that ε is feasible for a new LFM signal component declaration, it is advisable to set the value of ε equal to the average power of the background clutter surrounding the moving targets for the residual clutter suppression.…”

“…Ground-moving-target indication (GMTI) in a synthetic aperture radar (SAR) system has attracted much interest from wide-area traffic monitoring, as well as military surveillance activities due to its long-range, all-day, and all-weather imaging capability [1][2][3][4]. However, the SAR system is typically designed for imaging stationary scenes, so when a target is in motion, it can cause the defocusing and misplacement of the target in the SAR image.…”

“…(1) We designed the MLFMSS-Net based on a convolutional encoder-decoder architecture to separate the most-energetic LFM signal component from the multicomponent LFM signal with additive perturbation. The network exhibited strong robustness and low dependence on the system parameters, making it more suitable for practical imaging tasks; (2) We propose a MLFMSS-Net-assisted multi-moving-target-imaging method for the SAR-GMTI system. Given the unknown target number, an iterative signal-separation framework based on the trained MLFMSS-Net is presented to separate the multimoving-target signal into multiple LFM signal components while eliminating the residual clutter.…”

Dark Spot Detection from SAR Images Based on Superpixel Deeper Graph Convolutional Network

“…In particular, developed countries are investing a great deal of money in establishing oil spill monitoring systems to patrol, inspect, and manage offshore economic zones and territorial waters [3,4]. Synthetic Aperture Radar (SAR), which has the ability to penetrate clouds and fog and can work all day, is presently the most effective tool for oil pollution detection [1,5]. As oil spills on the sea surface cause the attenuation of the Bragg waves and reduce the roughness of the sea surface, the oil film generally appears as dark spots on SAR images [6].…”