Compressed sensing techniques have been applied to through-the-wall radar imaging (TWRI) and multipolarization TWRI for fast data acquisition and enhanced target localization. The studies so far in this area have either assumed effective wall clutter removal prior to image formation or performed signal estimation, wall clutter mitigation, and image formation independently. This paper proposes a low-rank and sparse imaging model for jointly addressing the problem of wall clutter mitigation and image formation in multichannel TWRI. The proposed model exploits two important structures of through-wall radar signals: low-rank structure of the wall reflections and jointly-sparse structure among the different polarization images. The task of removing wall clutter and reconstructing multichannel images of the same scene behind-the-wall is formulated as a regularized least squares problem, where low-rank regularization is enforced for the wall components, and joint-sparsity penalty is imposed on channel images. To solve the optimization problem, an iterative algorithm based on the proximal gradient technique is introduced, which simultaneously estimates the wall interferences and yields multichannel images of the indoor targets. Experiments on real and simulated radar data are conducted under full measurements and compressive sensing scenarios. The results show that the proposed model is very effective at removing unwanted wall clutter and enhancing the stationary targets, even under considerable reduction in measurements.

Abstract. We introduce a robust image-formation approach for through-the-wall radar imaging (TWRI). The proposed approach consists of two stages involving compressive sensing (CS) followed by delay-and-sum (DS) beamforming. In the first stage, CS is used to reconstruct a complete set of measurements from a small subset collected with a reduced number of transceivers and frequencies. DS beamforming is then applied to form the image using the reconstructed measurements. To promote sparsity of the CS solution, an overcomplete Gabor dictionary is employed to sparsely represent the imaged scene. The new approach requires far fewer measurement samples than the conventional DS beamforming and CSbased TWRI methods to reconstruct a high-quality image of the scene. Experimental results based on simulated and real data demonstrate the effectiveness and robustness of the proposed two-stage image formation technique, especially when the measurement set is drastically reduced. 1 Introduction Through-the-wall radar imaging (TWRI) is an emerging technology with considerable research interest and important applications in surveillance and reconnaissance for both civilian and military missions. [1][2][3][4][5][6] To deliver high-resolution radar images in both range and crossrange, TWRI systems use wideband signals and large aperture arrays (physical or synthetic). This leads to prolonged data acquisition and high computational complexity because a large number of samples need to be processed. New approaches for TWRI are therefore needed to obtain high-quality images from fewer data samples at a faster speed. To this end, this paper proposes a new approach using compressive sensing (CS) for through-the-wall radar imaging. CS is used here to reconstruct a full measurement set, which is then employed for image formation using delay-and-sum (DS) beamforming.CS enables a sparse signal to be reconstructed using considerably fewer data samples than what is required by the Nyquist-Shannon theorem. [7][8][9] In through-the-wall radar

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.

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This paper addresses the problem of wall clutter mitigation in compressed sensing through-the-wall radar imaging, where a different set of frequencies is sensed at different antenna locations. A joint Bayesian sparse approximation framework is first employed to reconstruct all the signals simultaneously by exploiting signal sparsity and correlations between antenna signals. This is in contrast to previous approaches where the signal at each antenna location is reconstructed independently. Furthermore, to promote sparsity and improve signal reconstruction accuracy, a sparsifying wavelet dictionary is employed in the sparse signal recovery. Following signal reconstruction, a subspace projection technique is applied to remove wall clutter, prior to image formation. Experimental results on real data show that the proposed approach produces significantly higher reconstruction accuracy and requires far fewer measurements compared to the single-signal model, where each antenna signal is reconstructed independently.

In this paper, a distributed compressive sensing (CS) model is proposed to recover missing data samples along the temporal frequency domain for through-the-wall radar imaging (TWRI). Existing CS-based approaches recover the signal from each antenna independently, without considering the correlations among measurements. The proposed approach, on the other hand, exploits the structure or correlation in the signals received across the array aperture by using a hierarchical Bayesian model to learn a shared prior for the joint reconstruction of the high-resolution radar profiles. A backprojection method is then applied to form the radar image. Experimental results on real TWRI data show that the proposed approach produces better radar images using fewer measurements compared to existing CS-based TWRI methods. (2013) ABSTRACTIn this paper, a distributed compressive sensing (CS) model is proposed to recover missing data samples along the temporal frequency domain for through-the-wall radar imaging (TWRI). Existing CS-based approaches recover the signal from each antenna independently, without considering the correlations among measurements. The proposed approach, on the other hand, exploits the structure or correlation in the signals received across the array aperture by using a hierarchical Bayesian model to learn a shared prior for the joint reconstruction of the highresolution radar profiles. A backprojection method is then applied to form the radar image. Experimental results on real TWRI data show that the proposed approach produces better radar images using fewer measurements compared to existing CS-based TWRI methods.

This paper presents a new image formation method for multi-polarization through-the-wall radar imaging. The proposed method combines wall clutter mitigation and scene reconstruction in a unified framework using multitask Bayesian compressed sensing. First, the radar signals are jointly recovered using Bayesian compressed sensing in the wavelet domain. Then, a subspace projection method is employed to mitigate the front wall reflections. This is followed by principal component analysis, which is used to compress the remaining wavelet coefficients and remove noise. A linear model is developed which relates the compressed wavelet coefficients directly to the image of the scene. For scene reconstruction, multitask Bayesian compressed sensing is further applied to simultaneously form the images associated with all polarimetric channels. Experimental results based on real radar data demonstrate that the proposed method improves image quality by enhancing target reflections and attenuating background clutter. Abstract-This paper presents a new image formation method for multi-polarization through-the-wall radar imaging. The proposed method combines wall clutter mitigation and scene reconstruction in a unified framework using multitask Bayesian compressed sensing. First, the radar signals are jointly recovered using Bayesian compressed sensing in the wavelet domain. Then, a subspace projection method is employed to mitigate the front wall reflections. This is followed by principal component analysis, which is used to compress the remaining wavelet coefficients and remove noise. A linear model is developed which relates the compressed wavelet coefficients directly to the image of the scene. For scene reconstruction, multitask Bayesian compressed sensing is further applied to simultaneously form the images associated with all polarimetric channels. Experimental results based on real radar data demonstrate that the proposed method improves image quality by enhancing target reflections and attenuating background clutter. I. INTRODUCTIONThrough-the-wall radar imaging (TWRI) is emerging as a viable sensing technology supporting a range of civilian and military applications, such as search-and-rescue, law enforcement, and urban surveillance and reconnaissance. It can be used for various purposes, including determining the building layout, and locating and identifying stationary objects behind walls. In the past years, numerous studies have been conducted in modeling and imaging stationary and moving targets behind walls and inside enclosed building structures [1]- [4]. However, there is still a need for producing high quality images that can effectively discriminate the targets of interest from clutter without increasing the data acquisition time.Several TWRI studies have focused on the use of polarization of the electromagnetic (EM) waves for detecting targets or enhancing the discrimination of targets [5]-[11]. In [5], a method for through-the-wall detection of certain types of weapons was developed by analyzing the...