Enhanced wall clutter mitigation for compressed through-the-wall radar imaging using joint Bayesian sparse signal recovery

2015 IEEE International Conference on Digital Signal Processing (DSP)

Tivive

2015

This paper addresses the problem of wall clutter mitigation in through-the-wall radar imaging using compressed sensing. In the proposed method, the radar signals are recovered using a joint Bayesian sparse representation, and the estimated coefficients are transformed into a third-order data tensor. Then, higherorder singular value decomposition (HOSVD) is applied to form a multilinear wall subspace. To remove the returns associated with wall clutter, the radar signal is projected onto the complement of the wall subspace. Furthermore, a compact image formation model is developed using principal component analysis (PCA), which yields a smaller dictionary size and reduced noise. Experimental results show that the proposed HOSVD-based method outperforms the standard SVD-based wall clutter mitigation technique. Abstract-This paper addresses the problem of wall clutter mitigation in through-the-wall radar imaging using compressed sensing. In the proposed method, the radar signals are recovered using a joint Bayesian sparse representation, and the estimated coefficients are transformed into a third-order data tensor. Then, higher-order singular value decomposition (HOSVD) is applied to form a multilinear wall subspace. To remove the returns associated with wall clutter, the radar signal is projected onto the complement of the wall subspace. Furthermore, a compact image formation model is developed using principal component analysis (PCA), which yields a smaller dictionary size and reduced noise. Experimental results show that the proposed HOSVDbased method outperforms the standard SVD-based wall clutter mitigation technique.

Section: A Joint Signal Coefficient Estimationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Wall clutter mitigation using HOSVD in through-the-wall radar imaging with compressed sensing

2015 IEEE International Conference on Digital Signal Processing (DSP)

Tivive

2015

“…In TWR imaging, most CS techniques [7][8][9][10][11] assume that the wall returns can be completely removed before applying CS, or a background scene is available for suppressing the wall reflections. Very recently, wall mitigation techniques were investigated in the CS context [12][13][14]. In compressed TWR sensing, the same frequency observations may not be available at different antennas due to competing wireless services, intentional interferences, or radar jamming [15].…”

Section: Introductionmentioning

confidence: 99%

“…Therefore, most existing CSbased approaches for wall-clutter mitigation are performed in two stages. In the first stage, the antenna signals are recovered from reduced data samples using ℓ1 minimization [12], joint Bayesian sparse approximation [13], or block-sparse estimation [14]. In the second stage, the wall reflections are removed by applying clutter mitigation techniques, such as spatial filtering [16], or subspace projection [17,18], prior to image formation.…”

Section: Introductionmentioning

confidence: 99%

Radar imaging of stationary indoor targets using joint low-rank and sparsity constraints

Tang

2016 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

Phung

et al. 2016

Self Cite

This paper introduces a joint low-rank and sparsity-based model to address the problem of wall-clutter mitigation in compressed through-the-wall radar imaging. The proposed model is motivated by two observations that wall reflections reside in a low-rank subspace, and target signals tend to be sparse. In the proposed approach, the task of segregating target returns from wall reflections is formulated as a joint low-rank and sparsity constrained optimization problem. Here, the low rank constraint is imposed on the wall component and the sparsity constraint is used to model the target component. An iterative soft thresholding algorithm is developed to estimate a low-rank matrix of wall clutter and a sparse matrix of target reflections from a reduced measurement set. Once the wall and target components are estimated, the target signals are used for scene reconstruction. Experimental evaluation was conducted using real radar data. The results show that the proposed model is very effective at removing wall clutter and reconstructing the image of behind-thewall targets from reduced measurements. ABSTRACTThis paper introduces a joint low-rank and sparsity-based model to address the problem of wall-clutter mitigation in compressed through-the-wall radar imaging. The proposed model is motivated by two observations that wall reflections reside in a low-rank subspace, and target signals tend to be sparse. In the proposed approach, the task of segregating target returns from wall reflections is formulated as a joint low-rank and sparsity constrained optimization problem. Here, the low rank constraint is imposed on the wall component and the sparsity constraint is used to model the target component. An iterative soft thresholding algorithm is developed to estimate a low-rank matrix of wall clutter and a sparse matrix of target reflections from a reduced measurement set. Once the wall and target components are estimated, the target signals are used for scene reconstruction. Experimental evaluation was conducted using real radar data. The results show that the proposed model is very effective at removing wall clutter and reconstructing the image of behind-the-wall targets from reduced measurements.

“…Here, a sparse Bayesian recovery framework is employed for jointly estimating the coefficient vectors θ l,m since it is more suitable for the TWRI problem than other simultaneous recovery algorithms. 13 In the Bayesian model, the noise term in (11) is assumed to be zero-mean Gaussian with independent and identically distributed (i.i.d) components. Thus, the probability density function (pdf) of ǫ l,m can be expressed as…”

Section: Joint Signal Coefficient Estimationmentioning

confidence: 99%

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

Tang

Phung

et al. 2015

Compressive Sensing IV

Self Cite

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.