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

Çetin, Müjdat; Stojanović, Ivana; Önhon, Özben Naime; Varshney, Kush R.; Samadi, Sadegh; Karl, W.C.; Willsky, Alan S.

doi:10.1109/msp.2014.2312834

Cited by 257 publications

(131 citation statements)

References 52 publications

(83 reference statements)

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“…Since phase components are not sparse due to them being uniformly distributed in the interval of [−π, π], we often model and represent sparsity in radar images based on their amplitude components alone. However, the complex nature of original images and the mechanism of coherent radar imaging require treatment of both amplitude and phase [55], even if we need amplitude images only as the end results. Nevertheless, there remains the issue of optimum sparse representation and sparsity/feature-enhanced imaging algorithms for compressible radar scenes, as elaborated below.…”

Section: Discussionmentioning

confidence: 99%

“…The so-called sparsity-enhanced methods for compressive radar imaging may be usefully explored, as described by [55]. The cost functional to minimize includes sparsity-enforcing weights on the vectors of amplitudes and their gradients, in addition to an l2-norm on the differences between complex-valued measurements Y and reconstruction-induced projections AX.…”

Section: Discussionmentioning

confidence: 99%

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Information-Theoretic Characterization and Undersampling Ratio Determination for Compressive Radar Imaging in a Simulated Environment

Zhang

Yang

Liu

et al. 2015

Entropy

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Abstract:Assuming sparsity or compressibility of the underlying signals, compressed sensing or compressive sampling (CS) exploits the informational efficiency of under-sampled measurements for increased efficiency yet acceptable accuracy in information gathering, transmission and processing, though it often incurs extra computational cost in signal reconstruction. Shannon information quantities and theorems, such as source rate-distortion, trans-information and rate distortion theorem concerning lossy data compression, provide a coherent framework, which is complementary to classic CS theory, for analyzing informational quantities and for determining the necessary number of measurements in CS. While there exists some information-theoretic research in the past on CS in general and compressive radar imaging in particular, systematic research is needed to handle issues related to scene description in cluttered environments and trans-information quantification in complex sparsity-clutter-sampling-noise settings. The novelty of this paper lies in furnishing a general strategy for information-theoretic analysis of scene compressibility, trans-information of radar echo data about the scene and the targets of interest, respectively, and limits to undersampling ratios necessary for scene reconstruction subject to distortion given sparsity-clutter-noise constraints. A computational experiment was performed to demonstrate informational analysis regarding the scene-sampling-reconstruction process and to generate phase transition diagrams showing relations between undersampling ratios and sparsity-clutter-noise-distortion constraints. The strategy proposed in this paper is valuable for information-theoretic analysis and undersampling theorem developments in compressive

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Information-Theoretic Characterization and Undersampling Ratio Determination for Compressive Radar Imaging in a Simulated Environment

Zhang

Yang

Liu

et al. 2015

Entropy

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“…Since the radar echo reflected from man-made moving targets are usually stronger than the background, in recent years, many sparsity-aware methods have been applied to SAR moving target parameter estimation and imaging [18][19][20][21][22][23][24][25]. Ref.…”

Section: Introductionmentioning

confidence: 99%

“…Ref. [23,25] summarize the latest application of sparse processing in SAR systems. An approach to motion parameter estimation with low pulse repetition frequency based on compressed sensing (CS) theory is proposed in [19].…”

Section: Introductionmentioning

confidence: 99%

Refocusing of Moving Targets in SAR Images via Parametric Sparse Representation

Chen

Zhang

et al. 2017

Remote Sensing

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Abstract:In this paper, a parametric sparse representation (PSR) method is proposed for refocusing of moving targets in synthetic aperture radar (SAR) images. In regular SAR images, moving targets are defocused due to unknown motion parameters. Refocusing of moving targets requires accurate phase compensation of echo data. In the proposed method, the region of interest (ROI) data containing the moving targets are extracted from the complex SAR image and represented in a sparse fashion through a parametric transform, which is related to the phase compensation parameter. By updating the reflectivities of moving target scatterers and the parametric transform in an iterative fashion, the phase compensation parameter can be accurately estimated and the SAR images of moving targets can be refocused well. The proposed method directly operates on small-size defocused ROI data, which helps to reduce the computational burden and suppress the clutter. Compared to other existing ROI-based methods, the proposed method can suppress asymmetric side-lobes and improve the image quality. Both simulated data and real SAR data collected by GF-3 satellite are used to validate the effectiveness of the proposed method.

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“…Therefore, how to shorten the data acquisition time of SFGPR system and improve imaging speed become an urgent problem to be solved. In recent years, Donoho and Candès et al have developed an information theory, known as compressed sensing (CS) theory [6,7], which is gaining increasing interest in the field of radar imaging [8]. Under the framework of CS theory, signal sampling can greatly break through the limitation of Nyquist sampling theorem, which can bring a great convenience for the signal acquisition, storage, transmission and processing.…”

Section: Introductionmentioning

confidence: 99%

Compressive Sensing SFGPR Imaging Algorithm Based on Subspace Projection Ground Clutter Suppression

Sun¹,

Lu²,

Zhang³

2016

PIER M

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Abstract-Stepped frequency ground penetrating radar (SFGPR) has received increasing attention in the field of ground penetrating radar technology due to its superiority in the detection performance. Compressed sensing (CS) SFGPR imaging reconstruction method can not merely reduce the measured imaging data volume, but also reconstruct target image with less sidelobe. However, the imaging algorithm using CS approach will lose efficacy in strong clutter environment. To solve this problem, a CS SFGPR imaging reconstruction method combined with subspace projection clutter suppression approach is proposed in this paper. First, all frequency domain data at each measurement position are reconstructed from reduced frequency measurements via sparse reconstruction technique. Then subspace projection ground clutter suppression technique is used to suppress the strong ground clutter. Finally, the orthogonal matching pursuit (OMP) algorithm is utilized to reconstruct the underground target image. Synthetic and experimental data processing results have verified the effectiveness and accuracy of the proposed imaging method.

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

Cited by 257 publications

References 52 publications

Information-Theoretic Characterization and Undersampling Ratio Determination for Compressive Radar Imaging in a Simulated Environment

Information-Theoretic Characterization and Undersampling Ratio Determination for Compressive Radar Imaging in a Simulated Environment

Refocusing of Moving Targets in SAR Images via Parametric Sparse Representation

Compressive Sensing SFGPR Imaging Algorithm Based on Subspace Projection Ground Clutter Suppression

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