4 May 2016 Target detection in GPR data using joint low-rank and sparsity constraints
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Abstract
In ground penetrating radars, background clutter, which comprises the signals backscattered from the rough, uneven ground surface and the background noise, impairs the visualization of buried objects and subsurface inspections. In this paper, a clutter mitigation method is proposed for target detection. The removal of background clutter is formulated as a constrained optimization problem to obtain a low-rank matrix and a sparse matrix. The low-rank matrix captures the ground surface reflections and the background noise, whereas the sparse matrix contains the target reflections. An optimization method based on split-Bregman algorithm is developed to estimate these two matrices from the input GPR data. Evaluated on real radar data, the proposed method achieves promising results in removing the background clutter and enhancing the target signature.
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Abdesselam Bouzerdoum, Fok Hing Chi Tivive, Canicious Abeynayake, "Target detection in GPR data using joint low-rank and sparsity constraints", Proc. SPIE 9857, Compressive Sensing V: From Diverse Modalities to Big Data Analytics, 98570A (4 May 2016); doi: 10.1117/12.2228345; https://doi.org/10.1117/12.2228345
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