This paper explores a low-rank and sparse representation based technique to remove the clutter produced by rough ground surface for air-coupled ground penetrating radar (GPR). For rough ground surface, the surface clutter components in different A-Scan traces are not aligned on the depth axis. To compensate for the misalignment effect and facilitate clutter removal, the A-Scan traces are aligned using cross-correlation technique first. Then the low-rank and sparse representation approach is applied to decompose the GPR data into a low-rank matrix whose columns record the ground clutter in A-Scan traces upon alignment adjustment, and a sparse matrix that features the subsurface object under test. The effectiveness of the proposed clutter removal method has been evaluated through simulations.
Yu Zhang, Dylan Burns, Dan Orfeo, Dryver R. Huston, and Tian Xia, "Rough ground surface clutter removal in air-coupled ground penetrating radar data using low-rank and sparse representation," Proc. SPIE 10169, Nondestructive Characterization and Monitoring of Advanced Materials, Aerospace, and Civil Infrastructure 2017, 1016904 (Presented at SPIE Smart Structures and Materials + Nondestructive Evaluation and Health Monitoring: March 26, 2017; Published: 19 April 2017); https://doi.org/10.1117/12.2261355.
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