Joint Wall Mitigation and Compressive Sensing for Indoor Image Reconstruction

Lagunas, Eva; Amin, Moeness G.; Ahmad, Fauzia; Nájar, Montse

doi:10.1109/tgrs.2012.2203824

Cited by 91 publications

(89 citation statements)

References 40 publications

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“…5 shows the true positions of nine targets in the observed scene. The imaged scene is chosen to be have a downrange of [1,7] m and a crossrange of [−2, 2] m and divided into 121 × 81 spatial grids with an interval of 0.05 m in both downrange and crossrange. At each measurement position, 200 frequency measurement ranging from 2 GHz to 2.995 GHz with a step of 5 MHz are utilized for imaging reconstruction.…”

Section: Resultsmentioning

confidence: 99%

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Efficient Sparse Imaging Reconstruction Algorithm for Through-the-Wall Radar

Qu¹,

Xing²,

Yang³

2017

PIER C

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Abstract-Sparse reconstruction technique can be used to provide high-resolution imaging result for through-the-wall radar (TWR) system. Since conventional sparse imaging reconstruction algorithms usually require a tremendous amount of computer memory and computational complexity, it is very difficult to apply in the practical large-scale TWR imaging applications. To solve the above problem, an efficient sparse imaging reconstruction algorithm is proposed in this paper. The proposed imaging method combines the spectral projection gradient L1-norm (SPGL1) algorithm with nonuniform fast Fourier transform (NUFFT) technique to achieve imaging reconstruction. Benefiting from the function handle operation of SPGL1 and computational efficiency of NUFFT, the proposed imaging algorithm can significantly reduce the memory requirement and computation complexity. The simulated and experimental results have shown that the proposed imaging method can significantly reduce the required computer memory and computational cost while providing the similar recovered image quality as the conventional sparse imaging method.

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Section: Resultsmentioning

confidence: 99%

“…The first application of CS to TWR imaging was presented in [2] and further developed in [3][4][5][6]. In particular, a number of robust sparse TWR imaging algorithms have been proposed for hostile environments such as strong front wall clutter [7,8] and multipath echo interference [9].…”

Section: Introductionmentioning

confidence: 99%

Efficient Sparse Imaging Reconstruction Algorithm for Through-the-Wall Radar

Qu¹,

Xing²,

Yang³

2017

PIER C

View full text Add to dashboard Cite

show abstract

“…CS has been applied to two-dimensional (2D) radar imaging to reduce both the number of frequencies and the number of antenna elements (or the number of spatial samples in the case of a synthetic aperture) required for data collection [1][2][3][4][5][6]. The frequency and element positions are randomized to overcome aliasing in the downrange and grating lobes in the radiation pattern, respectively.…”

Section: Introductionmentioning

confidence: 99%

Application of Compressive Sensing to Two-Dimensional Radar Imaging Using a Frequency-Scanned Microstrip Leaky Wave Antenna

Yang¹,

Ling²

2017

Journal of electromagnetic engineering and science

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The application of compressive sensing (CS) to a radar imaging system based on a frequency-scanned microstrip leaky wave antenna is investigated. First, an analytical model of the system matrix is formulated as the basis for the inversion algorithm. Then, L1-norm minimization is applied to the inverse problem to generate a range-azimuth image of the scene. Because of the antenna length, the near-field effect is considered in the CS formulation to properly image close-in targets. The resolving capability of the combined frequency-scanned antenna and CS processing is examined and compared to results based on the short-time Fourier transform and the pseudo-inverse. Both simulation and measurement data are tested to show the system performance in terms of image resolution. This is an Open-Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/3.0) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. ⓒ

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“…In the proposed approach, a joint Bayesian sparse model is first employed to reconstruct the antenna signal coefficients simultaneously, by exploiting both the sparsity and correlations between antenna signals. This joint model differs from the single-signal CS recovery model presented in, 12,14 where each antenna signal is recovered independently. This paper demonstrates that the proposed joint Bayesian CS model requires far fewer measurements and yields higher reconstruction accuracy than the single-signal CS model.…”

Section: Introductionmentioning

confidence: 99%

Multi-view TWRI scene reconstruction using a joint Bayesian sparse approximation model

Tang

Bouzerdoum

Phung

et al. 2015

Compressive Sensing IV

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This paper addresses the problem of scene reconstruction in conjunction with wall-clutter mitigation for compressed multi-view through-the-wall radar imaging (TWRI). We consider the problem where the scene behindthe-wall is illuminated from different vantage points using a different set of frequencies at each antenna. First, a joint Bayesian sparse recovery model is employed to estimate the antenna signal coefficients simultaneously, by exploiting the sparsity and inter-signal correlations among antenna signals. Then, a subspace-projection technique is applied to suppress the signal coefficients related to the wall returns. Furthermore, a multi-task linear model is developed to relate the target coefficients to the image of the scene. The composite image is reconstructed using a joint Bayesian sparse framework, taking into account the interview dependencies. Experimental results are presented which demonstrate the effectiveness of the proposed approach for multi-view imaging of indoor scenes using a reduced set of measurements at each view. ABSTRACTThis paper addresses the problem of scene reconstruction in conjunction with wall-clutter mitigation for compressed multi-view through-the-wall radar imaging (TWRI). We consider the problem where the scene behindthe-wall is illuminated from different vantage points using a different set of frequencies at each antenna. First, a joint Bayesian sparse recovery model is employed to estimate the antenna signal coefficients simultaneously, by exploiting the sparsity and inter-signal correlations among antenna signals. Then, a subspace-projection technique is applied to suppress the signal coefficients related to the wall returns. Furthermore, a multi-task linear model is developed to relate the target coefficients to the image of the scene. The composite image is reconstructed using a joint Bayesian sparse framework, taking into account the inter-view dependencies. Experimental results are presented which demonstrate the effectiveness of the proposed approach for multi-view imaging of indoor scenes using a reduced set of measurements at each view.

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Joint Wall Mitigation and Compressive Sensing for Indoor Image Reconstruction

Cited by 91 publications

References 40 publications

Efficient Sparse Imaging Reconstruction Algorithm for Through-the-Wall Radar

Efficient Sparse Imaging Reconstruction Algorithm for Through-the-Wall Radar

Application of Compressive Sensing to Two-Dimensional Radar Imaging Using a Frequency-Scanned Microstrip Leaky Wave Antenna

Multi-view TWRI scene reconstruction using a joint Bayesian sparse approximation model

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