GPR Target Detection by Joint Sparse and Low-Rank Matrix Decomposition

Abstract

Ground penetrating radar (GPR) uses electromagnetic waves to image, locate, and identify changes in electric and magnetic properties in the ground. The received signal comprises not only the target echoes but also strong reflections from the rough, uneven ground surface, which impair subsurface inspections and visualization of buried objects. In this paper, a background clutter mitigation and target detection method using low-rank and sparse priors is proposed for GPR data. The radar signal is decomposed into the sum of a low-rank component and a sparse component, plus noise. The low-rank component captures the ground surface reflections and background clutter, whereas the sparse component contains the target reflections. The effectiveness of the proposed method is evaluated on real radar signals collected from buried landmines and improvised explosive devices. The experimental results show that the proposed method successfully removes the background clutter and estimates the target signals.

GPR Antipersonnel Mine Detection Based on Tensor Robust Principal Analysis

“…The reasons why TRPCA performs well in the environment with heavy clutter are due to two aspects: (1) the low-rank and sparse properties of the GPR images, and (2) the frequency dependency of target response. Since the model and theory of TRPCA are consistent with RPCA [23], the low-rank and sparse structure of GPR image can be utilized by the TRPCA for the separation of target image and clutter in each sub-band image (frontal slice), which is similar to the RPCA-based methods [12,21]. The proposed method uses imaging to enhance the sparsity and intensity of a target response, which generally leads to a better decomposition [19].…”

“…where X 1 , X 2 , and X 3 are the clutter, the target response, and noise, respectively. As mentioned above, the clutter that consist of ground reflection and antenna corss-talk is considered as low-rank component in the B-scan, whereas the target response are contained in sparse component [12,21,31]. Therefore, the low-rank and sparse property of GPR image data is utilized to discriminate the clutter X 1 and the target response X 2 by RPCA decomposition.…”

“…Then, we stack the sub-band images as frontal slices, that is, X (:, :, 1) = X LF and X (:, :, 2) = X HF , to create an image tensor resembling a hyperspectral image cube [32], where the spatial characteristics lies in the first two dimensions and the spectral information is contained in the third dimension. According to the low-rank and sparse prior of GPR data [12,21], the obtained GPR image tensor X can be represented as…”

“…Many efforts have been devoted to developing clutter mitigation methods, and these methods can be briefly categorized into three types: model-based methods, filtering, and subspace projection [11][12][13].…”

“…For GPR data traces, the ground clutter as well as antenna coupling are considered as low-rank, while the target responses are assumed to be contained in the sparse component [20]. Hence, the clutter suppression problem is cast as a low-rank and sparse representation problem and can be solved by the RPCA-based method [12,21]. The recently proposed Tensor Robust Principal Component Analysis (TRPCA) [22,23] provides an alternative perspective of solving the low-rank and sparse decomposition problem.…”