Efficient Compressed Sensing Method for Moving-Target Imaging by Exploiting the Geometry Information of the Defocused Results

Zhang, Xuepan; Liao, Guisheng; Zhu, Shengqi; Dong, Yuan; Du, Wentao

doi:10.1109/lgrs.2014.2349035

Cited by 33 publications

(3 citation statements)

References 18 publications

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“…In recent years, the application of compress sensing (CS) theory to SAR imaging has been rapidly developed [18][19][20][21][22]. In general, the number of moving targets in a SAR observation scene is finite, and thus the GMT echo signal satisfies sparsity, which allows the use of CS theory for imaging or parameter estimation of the compressed echo data.…”

Section: Introductionmentioning

confidence: 99%

“…The method achieves parameter estimation and imaging of moving targets by extracting GMT through sparse decomposition and then estimating the velocities using sparsity as a constraint. Zhang et al [22] proposed an efficient imaging algorithm for GMT. However, these methods suffer from high computational complexity or low accuracy of parameter estimation.…”

Section: Introductionmentioning

confidence: 99%

“…With the development of deep learning, its application in SAR imaging and interpretation has also received the attention of many scholars at home and abroad [22][23][24][25][26]. Mu et al [27] proposed a moving target imaging network based on convolutional neural network (CNN), which takes a defocus moving target image as input and learns the focus of the moving target through the network.…”

Section: Introductionmentioning

confidence: 99%

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

Chen

Luo

et al. 2022

Remote Sensing

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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.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

Chen

Luo

et al. 2022

Remote Sensing

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An Approach to 2D Signals Recovering in Compressive Sensing Context

Stanković

Orović

2016

Circuits Syst Signal Process

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In this paper we study the compressive sensing effects on 2D signals exhibiting sparsity in 2D DFT domain. A simple algorithm for reconstruction of randomly under-sampled data is proposed. It is based on the analytically determined threshold that precisely separates signal and non-signal components in the 2D DFT domain. The algorithm operates fast in a single iteration providing the accurate signal reconstruction. In the situations that are not comprised by the analytic derivation and constrains, the algorithm is still efficient and need just a couple of iterations. The proposed solution shows promising results in ISAR imaging (simulated data are used), where the reconstruction is achieved even in the case when less than 10% of data is available.

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Detection and fast motion parameter estimation for target with range walk effect based on new axis rotation moving target detection

Huang

Zhang

et al. 2022

Digital Signal Processing

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Efficient Compressed Sensing Method for Moving-Target Imaging by Exploiting the Geometry Information of the Defocused Results

Cited by 33 publications

References 18 publications

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

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

An Approach to 2D Signals Recovering in Compressive Sensing Context

Detection and fast motion parameter estimation for target with range walk effect based on new axis rotation moving target detection

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