Near-Field Radar Imaging via Compressive Sensing

Li, Shiyong; Zhao, Guoqiang; Li, Houmin; Ren, Bailing; Hu, Weidong; Liu, Yong; Yu, Weihua; Sun, Houjun

doi:10.1109/tap.2014.2381262

Cited by 39 publications

(14 citation statements)

References 19 publications

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“…with more information, the scattering pattern generated from the transmitting antenna is required as random as possible. With the controllable random surface, we could easily obtain a series of coding patterns that have the same information entropy, which might be of particular interests in the applications like compressive radar imaging31.…”

Section: Resultsmentioning

confidence: 99%

Flexible controls of scattering clouds using coding metasurfaces

Liu

Cui

2016

Sci Rep

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Metamaterials or metasurfaces have been designed to precisely manipulate the scattering at every angle. Here, we propose to control the probability of random scattering appearing in the desired range of angles, which is defined in this letter as scattering cloud. We present a controllable random metasurface by simply adding a random coding sequence to gradient coding sequence. It is shown that the direction and size of the scattering cloud can be arbitrarily engineered. We demonstrate the exotic behavior of the scattering cloud by making an analogy to the electron cloud in quantum mechanics. A new coding particle featuring low-interference with neighboring coding particles is designed to realize the controllable random surface, which demonstrates highly consistent results to the theoretical calculations using fast Fourier transform. The exciting phenomena and versatile behaviors of scattering clouds and their probabilities enabled by controllable random surfaces will lead to diversified applications in the fields of electromagnetic waves and acoustic waves.

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Section: Resultsmentioning

confidence: 99%

Flexible controls of scattering clouds using coding metasurfaces

Liu

Cui

2016

Sci Rep

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“…If the size of the sensing matrix is too large, we can use the method in [25] to change Φ into a 2-D Fourier transform with a compensation for the spherical wavefront. In addition, if the distance R 0 is sufficiently large, (13) can be approximated as R i (t m ) ≈ R 0 −x i sin ωt m +y i cos ωt m .…”

Section: Mrf-fista Applied To Isar Imagingmentioning

confidence: 99%

Radar Imaging by Sparse Optimization Incorporating MRF Clustering Prior

Amin

Zhao

et al. 2020

IEEE Geosci. Remote Sensing Lett.

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Recent progress in compressive sensing states the importance of exploiting intrinsic structures in sparse signal reconstruction. In this letter, we propose a Markov random field (MRF) prior in conjunction with fast iterative shrinkagethresholding algorithm (FISTA) for image reconstruction. The MRF prior is used to represent the support of sparse signals with clustered nonzero coefficients. The proposed approach is applied to the inverse synthetic aperture radar (ISAR) imaging problem. Simulations and experimental results are provided to demonstrate the performance advantages of this approach in comparison with the standard FISTA and existing MRF-based methods.Index Terms-Compressive sensing (CS), Markov random field (MRF), FISTA, inverse synthetic aperture radar (ISAR).

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“…However, the resulting high sampling rate poses difficulties for raw data transmission and storage. The recently-developed compressive sensing (CS) framework can reduce the measurements, but under the situation that the target is constructed by some sparsely-distributed scattering points, and the number of scattering points is much less than the number of imaging grids [8,9]. Additionally, the CS-based algorithms need to design a very accurate measurement matrix, and their recovery quality may be seriously affected by the accuracy of the measurement matrix, which is always influenced by system errors and off-grid error [10,11,12].…”

Section: Introductionmentioning

confidence: 99%

High Resolution Turntable Radar Imaging via Two Dimensional Deconvolution with Matrix Completion

Xia

Yin

et al. 2017

Sensors

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Resolution is the bottleneck for the application of radar imaging, which is limited by the bandwidth for the range dimension and synthetic aperture for the cross-range dimension. The demand for high azimuth resolution inevitably results in a large amount of cross-range samplings, which always need a large number of transmit-receive channels or a long observation time. Compressive sensing (CS)-based methods could be used to reduce the samples, but suffer from the difficulty of designing the measurement matrix, and they are not robust enough in practical application. In this paper, based on the two-dimensional (2D) convolution model of the echo after matched filter (MF), we propose a novel 2D deconvolution algorithm for turntable radar to improve the radar imaging resolution. Additionally, in order to reduce the cross-range samples, we introduce a new matrix completion (MC) algorithm based on the hyperbolic tangent constraint to improve the performance of MC with undersampled data. Besides, we present a new way of echo matrix reconstruction for the situation that only partial cross-range data are observed and some columns of the echo matrix are missing. The new matrix has a better low rank property and needs just one operation of MC for all of the missing elements compared to the existing ways. Numerical simulations and experiments are carried out to demonstrate the effectiveness of the proposed method.

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Near-Field Radar Imaging via Compressive Sensing

Cited by 39 publications

References 19 publications

Flexible controls of scattering clouds using coding metasurfaces

Flexible controls of scattering clouds using coding metasurfaces

Radar Imaging by Sparse Optimization Incorporating MRF Clustering Prior

High Resolution Turntable Radar Imaging via Two Dimensional Deconvolution with Matrix Completion

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