Auto-focus for under-sampled synthetic aperture radar

Kelly, Shaun I.; Yaghoobi, Mehrdad; Davies, Mike E.

doi:10.1049/ic.2012.0113

Cited by 5 publications

(3 citation statements)

References 10 publications

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“…More recently, [35] and [36] have used similar ideas to achieve autofocusing of undersampled SAR data. The method proposed in [35] is based on minimizing a constrained version of the cost functional in (12).…”

Section: Wide-angle Sar Imaging Of Anisotropic Scatteringmentioning

confidence: 99%

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Sparsity-Driven Synthetic Aperture Radar Imaging: Reconstruction, autofocusing, moving targets, and compressed sensing

Çetin

Stojanović

Önhon

et al. 2014

IEEE Signal Process. Mag.

263

125

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This paper presents a survey of recent research on sparsity-driven synthetic aperture radar (SAR) imaging.In particular, it reviews (i) analysis and synthesis-based sparse signal representation formulations for SAR image formation together with the associated imaging results; (ii) sparsity-based methods for wide-angle SAR imaging and anisotropy characterization; (iii) sparsity-based methods for joint imaging and autofocusing from data with phase errors; (iv) techniques for exploiting sparsity for SAR imaging of scenes containing moving objects, and (v) recent work on compressed sensing-based analysis and design of SAR sensing missions.

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Section: Wide-angle Sar Imaging Of Anisotropic Scatteringmentioning

confidence: 99%

“…The method proposed in [35] is based on minimizing a constrained version of the cost functional in (12). Optimization is performed through a three block relaxation approach by using an extra surrogate parameter for the field in order to guarantee convergence.…”

Section: Wide-angle Sar Imaging Of Anisotropic Scatteringmentioning

confidence: 99%

Sparsity-Driven Synthetic Aperture Radar Imaging: Reconstruction, autofocusing, moving targets, and compressed sensing

Çetin

Stojanović

Önhon

et al. 2014

IEEE Signal Process. Mag.

263

125

View full text Add to dashboard Cite

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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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