Skip to main content
placeholder image

Target detection in GPR data using joint low-rank and sparsity constraints

Conference Paper


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.

Publication Date


  • 2016

Citation


  • Bouzerdoum, A., Tivive, F. H. C., & Abeynayake, C. (2016). Target detection in GPR data using joint low-rank and sparsity constraints. In Proceedings of SPIE - The International Society for Optical Engineering Vol. 9857. doi:10.1117/12.2228345

Scopus Eid


  • 2-s2.0-84978654524

Volume


  • 9857

Issue


Place Of Publication


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.

Publication Date


  • 2016

Citation


  • Bouzerdoum, A., Tivive, F. H. C., & Abeynayake, C. (2016). Target detection in GPR data using joint low-rank and sparsity constraints. In Proceedings of SPIE - The International Society for Optical Engineering Vol. 9857. doi:10.1117/12.2228345

Scopus Eid


  • 2-s2.0-84978654524

Volume


  • 9857

Issue


Place Of Publication