Multipolarization Through-Wall Radar Imaging Using Low-Rank and Jointly-Sparse Representations

Abstract

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.

Through-the-Wall Target Separation Using Low-Rank and Variational Mode Decomposition

“…Since the direct wall returns and wall reverberations are stronger than the target reflections, they need to be removed or, at least, significantly suppressed before target separation. Therefore, to remove the wall reflections from the radar signal, the same technique described in [33] and [34] is adopted, i.e., capturing the direct wall returns and the wall reverberations in a low-rank matrix. Let Y denote a matrix containing radar signals as its columns.…”

“…In this paper, a target separation method is proposed for TWRI, which can estimate the wall reflections and segregate the different target returns. Since the wall reflections are highly correlated across the antennas, it is expected that they lie in a low-rank subspace, as mentioned in [33] and [34]. Moreover, the radar signal, which comprises delayed target echoes, can be modeled as a superposition of several narrow-band signals.…”

Improved Clutter Removal by Robust Principal Component Analysis for Chaos Through-Wall Imaging Radar

“…Recently, Wright et al [20] proposed a robust principal component analysis (RPCA) method (also called low-rank and sparse representation or sparse and low-rank decomposition) and showed that under rather broad conditions, the method can recover a low-rank matrix or a sparse matrix from highly corrupted measurements via decomposition. It has been demonstrated that RPCA is an effective clutter removal method in through-wall imaging applications [21][22][23][24]. For TWI radar, as the number of human targets is usually limited, the target responses are contained in a sparse matrix, and the clutters form a low-rank matrix.…”

“…Tang et al [22] presented a joint low-rank and sparsity-based method for compressed sensing TWI radar, which can mitigate the wall reflection and reconstruct an image of the scene even when the number of measurements is significantly reduced. Later, they proposed a low-rank and sparse imaging model to combine wall clutter mitigation with image formation for multipolarization TWI radar and verified its effectivity in removing unwanted wall clutter and enhancing the stationary targets under considerable reduction in measurements [23]. Qiu et al [24] applied the RPCA method to micromotion human indication in UWB through-wall radar, and verified that the method can remove most of the clutter and avoid introducing extra ringing artifacts.…”

Clutter Reduction and Target Tracking in Through-the-Wall Radar

“…Therefore, the joint low-rank and sparse approaches for clutter reduction and scene reconstruction have been developed based on compressed sensing (CS) technique [10]- [17]. Particularly, in [10]- [12], Tang et al used an iterative soft threshold method to solve the joint optimization problem, while in [15], the optimization problem was solved utilizing the mathematical toolbox TFOCS [18].…”