Adaptive Sparse Recovery by Parametric Weighted L$_{1}$ Minimization for ISAR Imaging of Uniformly Rotating Targets

Rao, Wei; Li, Gang; Wang, Xiqin; Xia, Xiang-Gen

doi:10.1109/jstars.2012.2215915

Cited by 69 publications

(40 citation statements)

References 28 publications

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“…The benefits provided by sparsitydriven imaging are even greater in such non-conventional sensing scenarios. Sparsity-driven imaging has also been used for the problem of inverse SAR (ISAR) imaging of rotating targets [16], as well as for through-the-wall radar imaging [17]. It has also been extended to interferometric SAR [18] and SAR tomography (TomoSAR) [19] adding the elevation direction into the problem for 3-D imaging, as well as to 4-D (differential, i.e., spacetime)…”

Section: Analysis and Synthesis-based Sparse Reconstruction For Sarmentioning

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.

256

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: Analysis and Synthesis-based Sparse Reconstruction For Sarmentioning

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.

256

125

View full text Add to dashboard Cite

show abstract

“…The CS framework has been successfully applied in conventional ISAR and bistatic ISAR imaging [24][25][26][27].…”

Section: Cs-based Passive Radar Super-resolution Imagingmentioning

confidence: 99%

Sparsity-Inducing Super-Resolution Passive Radar Imaging with Illuminators of Opportunity

Zhang

Wang

et al. 2016

Remote Sensing

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Multiple illuminators of opportunity (IOs) and a large rotation angle are often required for current passive radar imaging techniques. However, a large rotation angle demands a long observation time, which cannot be implemented for actual passive radar system. To overcome this disadvantage, this paper proposes a super-resolution passive radar imaging framework with a sparsity-inducing compressed sensing (CS) technique, which allows for fewer IOs and a smaller rotation angle. In the proposed imaging framework, the sparsity-based passive radar imaging is modeled mathematically, and the spatial frequencies and amplitudes of different scatterers on the target are recovered by the log-sum penalty function-based CS reconstruction algorithm. In doing so, a super-resolution passive radar imagery is obtained by the frequency searching approach. Simulation results not only validate that the proposed method outperforms existing super-resolution algorithms, such as ESPRIT and RELAX, especially in the cases with low signal-to-noise ratio (SNR) and limited number of measurements, but also have shown that our proposed method can perform robust reconstruction no matter if the target is on grid or not.

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“…In general, the scatterer extraction of the radar image can be carried out by the algorithms in [3][4][5][6][7][8][9][10][11][12][13][14][15][16]. In [16], the matching pursuits (MP) algorithm is used to extract the parameters of main high-energy scatterers for the recognition application.…”

Section: Scatterer Extractionmentioning

confidence: 99%

“…Unlike the existing features [4][5][6][7], the 2-D location parameters of main high-energy scatterers on an ISAR image provide the image with very sparse representations and illuminate the scattering model of a target. Furthermore, the scattering model is often proportionately changed with the target scale and remains more stable over the wide angular region than the scatterer's amplitude [11][12][13][14]. Therefore, 2-D location parameters of main highenergy scatterers can be less sensitive to the variations of the ISAR image and more robust for target recognition.…”

Section: Introductionmentioning

confidence: 99%

Target Recognition With Radar Images via Parameterized Dictionary Sets

Wang¹,

Gu²,

Shang³

et al. 2014

PIER C

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Abstract-Target recognition through the processing of high-resolution radar images has been an active research area in past decades. In this paper, dictionary sets parameterized by the two-dimensional (2-D) location parameters of main high-energy scatterers are considered to recognize the candidate targets. For this purpose, the scatterer extraction and orientation estimation of radar image are firstly provided in this paper. Furthermore, the recognition method based on the parameterized dictionary sets is subsequently proposed. Different from the existed recognition methods, only the sampled images at the 2-D location parameters of main high-energy scatterers are used in the proposed method. Consequently, the noise or clutter outside the sampling locations can be filtered, which results in more robust performance. Moreover, the 2-D location parameters are proportional to the geometrical structure, and the proposed method is adaptive to the scale variation of the target images. Simulated results are provided to demonstrate the proposed method.

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Adaptive Sparse Recovery by Parametric Weighted L$_{1}$ Minimization for ISAR Imaging of Uniformly Rotating Targets

Cited by 69 publications

References 28 publications

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

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

Sparsity-Inducing Super-Resolution Passive Radar Imaging with Illuminators of Opportunity

Target Recognition With Radar Images via Parameterized Dictionary Sets

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