Sparse Signal Methods for 3-D Radar Imaging

Austin, Christian D.; Ertin, Emre; Moses, Randolph L.

doi:10.1109/jstsp.2010.2090128

Cited by 102 publications

(61 citation statements)

References 47 publications

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“…The resurgent development of many theoretical analysis frameworks [22,23] and effective algorithms [24] has been witnessed. The applications of the sparse signal representation technique mainly include radar imaging [25,26], image restoration [27], image classification [28,29], and pattern recognition [15,30]. The key of sparse signal model is based on the fact that a certain signal can be represented by an overcomplete basis set (dictionary).…”

Section: Srcmentioning

confidence: 99%

SAR Image Recognition with Monogenic Scale Selection-Based Weighted Multi-task Joint Sparse Representation

et al. 2018

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Abstract:The monogenic signal, which is defined as a linear combination of a signal and its Riesz-transformed one, provides a great opportunity for synthetic aperture radar (SAR) image recognition. However, the incredibly large number of components at different scales may result in too much of a burden for onboard computation. There is great information redundancy in monogenic signals because components at some scales are less discriminative or even have negative impact on classification. In addition, the heterogeneity of the three types of components will lower the quality of decision-making. To solve the problems above, a scale selection method, based on a weighted multi-task joint sparse representation, is proposed. A scale selection model is designed and the Fisher score is presented to measure the discriminative ability of components at each scale. The components with high Fisher scores are concatenated to three component-specific features, and an overcomplete dictionary is built. Meanwhile, the scale selection model produces the weight vector. The three component-specific features are then fed into a multi-task joint sparse representation classification framework. The final decision is made in terms of accumulated weighted reconstruction error. Experiments on the Moving and Stationary Target Acquisition and Recognition (MSTAR) dataset have proved the effectiveness and superiority of our method.

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Section: Srcmentioning

confidence: 99%

SAR Image Recognition with Monogenic Scale Selection-Based Weighted Multi-task Joint Sparse Representation

et al. 2018

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“…Examples include SAR [2,33], inverse SAR (ISAR) [34][35][36], moving target indicator [7], etc. To introduce the relative movement between the proposed radar and targets, the placement of the MMWCSAR system is presented in section 2.1.…”

Section: Mmwcsar Systemmentioning

confidence: 99%

3D Imaging Millimeter Wave Circular Synthetic Aperture Radar

Zhang

Cao

2017

Preprint

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Abstract:In this paper, a new millimeter wave 3D imaging radar is proposed. The user just needs to move the radar along a circular track, a high resolution 3D imaging can be generated. The proposed radar uses the movement of itself to synthesize a large aperture in both the azimuth and elevation directions. It can utilize inverse Radon transform to resolve 3D imaging. To improve the sensing result, compressed sensing approach is further investigated. The simulation and experimental result further illustrated the design. Because a single transceiver circuit is needed, a light, affordable and high resolution 3D mmWave imaging radar is illustrated in the paper.

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“…Anisotropy characterization methods may be broadly categorized into those employing parameterizations for angle-dependent scattering in the phase history domain, multi-aperture methods that operate in the image domain, and sparsity-driven non-parametric image formation and anisotropy characterization methods. Within the third category, techniques either apply sparse reconstruction methods described in the previous section independently on a set of (possibly overlapping) small subapertures [8], [23]- [25] or jointly process wide-aperture data by incorporating prior information on aspect-dependent scattering [26]- [28]. The independent processing methods have similarities with image domain multi-aperture methods.…”

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.

252

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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Sparse Signal Methods for 3-D Radar Imaging

Cited by 102 publications

References 47 publications

SAR Image Recognition with Monogenic Scale Selection-Based Weighted Multi-task Joint Sparse Representation

SAR Image Recognition with Monogenic Scale Selection-Based Weighted Multi-task Joint Sparse Representation

3D Imaging Millimeter Wave Circular Synthetic Aperture Radar

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

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