Phase Error Correction for Approximated Observation-Based Compressed Sensing Radar Imaging

Li, Bo; Liu, Falin; Zhou, Chongbin; Lv, Yue; Hu, Jingqiu

doi:10.3390/s17030613

Cited by 11 publications

(11 citation statements)

References 20 publications

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“…This proves that the proposed one-step MOCO method can well compensate both range-variant motion error and residual RCM, however, there exists a high level of sidelobes in Figure 6 b. Figure 6 c shows the imaging result using a joint azimuth-range decoupled sparse imaging algorithm and an azimuth-varying phase-error correction method (method in [ 20 ]). The sparsity parameter is set as K = 6, the maximum iteration step is set as I = 40, and the determined threshold is

.…”

Section: Applications Resultsmentioning

confidence: 80%

“…The sparsity parameter is set as K = 6, the maximum iteration step is set as I = 40, and the determined threshold is

. It can be seen that the method in [ 20 ] fails to reconstruct points 1 and 2 in the right coordinate. This is because [ 20 ] only considered platform velocity error and can only compensate azimuth-varying phase errors.…”

Section: Applications Resultsmentioning

confidence: 99%

“…Both the proposed MOCO operator and the joint phase-error correction method can be directly added in this decoupled azimuth-range-based sparse SAR imaging method. Note that [ 20 ] has applied the two-step phase-error correction method on this azimuth-range decoupled sparse imaging method but still can only deal with the same azimuth phase errors in [ 12 , 13 , 14 , 15 , 16 , 17 ]. The proposed method has the following advantages: The proposed one-step MOCO operator can significantly reduce the residual RCM, hence improve the reconstruction accuracy of the azimuth-range decoupled sparse SAR imaging method and the focusing quality of SAR images.…”

Section: Introductionmentioning

confidence: 99%

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Correction of Range-Variant Motion Error and Residual RCM in Sparse Regularization SAR Imaging

Zhang

2022

Sensors

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Lq (0 < q ≤ 1) regularization has been confirmed effective when applied to sparse SAR imaging. However, the inaccuracies caused by motion errors in the observation model will lead to various degradations and defocus in the reconstructed image. For high-resolution and light-small SAR systems, the range-variant motion errors will decrease the accuracy of range cell migration correction (RCMC), and residual range cell migration (RCM) will exceed multiple range resolution cells and degrade the image quality substantially. Aiming at this problem, in this paper, a novel azimuth-range decoupled sparse SAR imaging method with coarse-to-fine range-variant motion errors and residual RCM correction method is proposed. First, a one-step motion compensation (MOCO) operator is proposed using the inertial navigation systems (INS)/global positioning systems (GPS) information, which can significantly reduce the residual RCM and improve the reconstruction accuracy. Second, a fine high-order phase-error correction method is performed to correct the range and cross-range-varying phase errors using a joint imaging and phase-error estimation scheme, which will further improve the image focusing quality. Experimental results indicate the effectiveness of the proposed method.

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.…”

Section: Applications Resultsmentioning

confidence: 80%

“…The sparsity parameter is set as K = 6, the maximum iteration step is set as I = 40, and the determined threshold is

Section: Applications Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Correction of Range-Variant Motion Error and Residual RCM in Sparse Regularization SAR Imaging

Zhang

2022

Sensors

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“…It can be used to reduce the amount of data transferred. Compressed sensing method has been applied in the context of medical imaging, radar imaging, and video transmission [2][3][4][5].…”

Section: Introductionmentioning

confidence: 99%

Improved Generalized Sparsity Adaptive Matching Pursuit Algorithm Based on Compressive Sensing

Zhao

Jia

2020

Journal of Electrical and Computer Engineering

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The modified adaptive orthogonal matching pursuit algorithm has a lower convergence speed. To overcome this problem, an improved method with faster convergence speed is proposed. In respect of atomic selection, the proposed method computes the correlation between the measurement matrix and residual and then selects the atoms most related to residual to construct the candidate atomic set. The number of selected atoms is the integral multiple of initial step size. In respect of sparsity estimation, the proposed method introduces the exponential function to sparsity estimation. It uses a larger step size to estimate sparsity at the beginning of iteration to accelerate the algorithm convergence speed and a smaller step size to improve the reconstruction accuracy. Simulations show that the proposed method has better performance in terms of convergence speed and reconstruction accuracy for one-dimension signal and two-dimension signal.

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“…In [ 12 ], a fast CS-based SAR imaging algorithm is proposed to save the computational cost both in time and memory. In [ 13 ], a phase error correction method is proposed for the CS-based radar imaging system based on approximated observation, which improves the defocus caused by the phase error. In [ 14 ], CS is utilized in tomographic SAR system, extended to the SAR elevation direction for 3-D imaging.…”

Section: Introductionmentioning

confidence: 99%

Distributed Compressed Sensing Based Ground Moving Target Indication for Dual-Channel SAR System

Liu

Tian

Jiang

et al. 2018

Sensors

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The dual-channel synthetic aperture radar (SAR) system is widely applied in the field of ground moving-target indication (GMTI). With the increase of the imaging resolution, the resulting substantial raw data samples increase the transmission and storage burden. We tackle the problem by adopting the joint sparsity model 1 (JSM-1) in distributed compressed sensing (DCS) to exploit the correlation between the two channels of the dual-channel SAR system. We propose a novel algorithm, namely the hierarchical variational Bayesian based distributed compressed sensing (HVB-DCS) algorithm for the JSM-1 model, which decouples the common component from the innovation components by applying variational Bayesian approximation. Using the proposed HVB-DCS algorithm in the dual-channel SAR based GMTI (SAR-GMTI) system, we can jointly reconstruct the dual-channel signals, and simultaneously detect the moving targets and stationary clutter, which enables sampling at a further lower rate in azimuth as well as improves the reconstruction accuracy. The simulation and experimental results show that the proposed HVB-DCS algorithm is capable of detecting multiple moving targets while suppressing the clutter at a much lower data rate in azimuth compared with the compressed sensing (CS) and range-Doppler (RD) algorithms.

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Phase Error Correction for Approximated Observation-Based Compressed Sensing Radar Imaging

Cited by 11 publications

References 20 publications

Correction of Range-Variant Motion Error and Residual RCM in Sparse Regularization SAR Imaging

Correction of Range-Variant Motion Error and Residual RCM in Sparse Regularization SAR Imaging

Improved Generalized Sparsity Adaptive Matching Pursuit Algorithm Based on Compressive Sensing

Distributed Compressed Sensing Based Ground Moving Target Indication for Dual-Channel SAR System

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