Autofocused compressive SAR imaging based on the alternating direction method of multipliers

Güngör, Alper; Çetin, Müjdat; Güven, H. Emre

doi:10.1109/radar.2017.7944458

Cited by 16 publications

(3 citation statements)

References 10 publications

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“…For instance, Onhon et al uses the pth power of the approximate l p norm as the regularization term and an alternating minimization framework to solve the problem [12]. There are also various methods addressing the problem in a compressive sensing context, such as the majorization-minimization-based method [13,14], iteratively re-weighted augmented Lagrangian-based method [15,16] and conjugate gradient-based method with a cost function involving hybrid regularization terms (approximate l 1 norm and approximate total variation regularization) [17].…”

Section: Introductionmentioning

confidence: 99%

Sparse Regularization with a Non-Convex Penalty for SAR Imaging and Autofocusing

et al. 2022

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In this paper, SAR image reconstruction with joint phase error estimation (autofocusing) is formulated as an inverse problem. An optimization model utilising a sparsity-enforcing Cauchy regularizer is proposed, and an alternating minimization framework is used to solve it, in which the desired image and the phase errors are estimated alternatively. For the image reconstruction sub-problem (f-sub-problem), two methods are presented that are capable of handling the problem’s complex nature. Firstly, we design a complex version of the forward-backward splitting algorithm to solve the f-sub-problem iteratively, leading to a complex forward-backward autofocusing method (CFBA). For the second variant, techniques of Wirtinger calculus are utilized to minimize the cost function involving complex variables in the f-sub-problem in a direct fashion, leading to Wirtinger alternating minimization autofocusing (WAMA) method. For both methods, the phase error estimation sub-problem is solved by simply expanding and observing its cost function. Moreover, the convergence of both algorithms is discussed in detail. Experiments are conducted on both simulated and real SAR images. In addition to the synthetic scene employed, the other SAR images focus on the sea surface, with two being real images with ship targets, and another two being simulations of the sea surface (one of them containing ship wakes). The proposed method is demonstrated to give impressive autofocusing results on these datasets compared to state-of-the-art methods.

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Section: Introductionmentioning

confidence: 99%

Sparse Regularization with a Non-Convex Penalty for SAR Imaging and Autofocusing

et al. 2022

View full text Add to dashboard Cite

show abstract

“…In recent years, many sparsity-driven algorithms [28][29][30][31][32][33][34] have been proposed to solve the defocusing problem and achieve effective performance. However, the references [28][29][30][31][32][33] cannot fully formulate the motion error and adopt the approximate expression so that the error is not removed. Reference 34 integrated with the deep SAR imaging algorithm to remove the motion error.…”

mentioning

confidence: 99%

Sub-Nyquist SAR Imaging And Error Correction Via Optimization-based Algorithm

Chen,

Zhang

2024

Preprint

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Sub-Nyquist synthetic aperture radar (SAR) based on pseudo-random time-space modulation has been proposed to increase swath width while preserving the azimuthal resolution. Due to the sub-Nyquist sampling, the scene can be recovered by the optimization-based algorithm. However, these methods suffer from some issues, e.g., manually tuning difficulty and pre-definition of optimization parameters, and low signal-noise-ratio (SNR) resistance. To address these issues, a reweighted optimization algorithm named pseudo-Λ0-norm optimization algorithm is proposed for the sub-Nyquist SAR system in this paper. A modified regularization model is first built by applying the scene prior information to nearly acquire the number of non-zero elements based on Bayesian estimation, and then this model is solved by the Cauchy-Newton method. Additionally, an error correction method combined with our proposed pseudo-Λ0-norm optimization algorithm is also present to eliminate defocusing for the motion-induced model. Finally, experiments with simulated signals and strip-map TerraSAR-X images are carried out to demonstrate the effectiveness and superiority of our proposed algorithm.

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“…In recent years, many sparsitydriven algorithms [28][29][30][31][32][33][34] have been proposed to solve the defocusing problem and achieve an effective performance. However, the references [28][29][30][31][32][33] do not fully formulate the motion error and adopt an approximate expression so that the error is not removed. The reference [34] integrated the deep SAR imaging algorithm to remove the motion error.…”

mentioning

confidence: 99%

Sub-Nyquist SAR Imaging and Error Correction Via an Optimization-Based Algorithm

Chen,

Zhang,

Xing

et al. 2024

Sensors

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Sub-Nyquist synthetic aperture radar (SAR) based on pseudo-random time–space modulation has been proposed to increase the swath width while preserving the azimuthal resolution. Due to the sub-Nyquist sampling, the scene can be recovered by an optimization-based algorithm. However, these methods suffer from some issues, e.g., manually tuning difficulty and the pre-definition of optimization parameters, and a low signal–noise ratio (SNR) resistance. To address these issues, a reweighted optimization algorithm, named pseudo-ℒ0-norm optimization algorithm, is proposed for the sub-Nyquist SAR system in this paper. A modified regularization model is first built by applying the scene prior information to nearly acquire the number of nonzero elements based on Bayesian estimation, and then this model is solved by the Cauchy–Newton method. Additionally, an error correction method combined with our proposed pseudo-ℒ0-norm optimization algorithm is also present to eliminate defocusing in the motion-induced model. Finally, experiments with simulated signals and strip-map TerraSAR-X images are carried out to demonstrate the effectiveness and superiority of our proposed algorithm.

show abstract

Autofocused compressive SAR imaging based on the alternating direction method of multipliers

Cited by 16 publications

References 10 publications

Sparse Regularization with a Non-Convex Penalty for SAR Imaging and Autofocusing

Sparse Regularization with a Non-Convex Penalty for SAR Imaging and Autofocusing

Sub-Nyquist SAR Imaging And Error Correction Via Optimization-based Algorithm

Sub-Nyquist SAR Imaging and Error Correction Via an Optimization-Based Algorithm

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