Assessment of statistical-based clutter reduction techniques on ground-coupled GPR data for the detection of buried objects in soils

A bi-static Ground Penetrating Radar (GPR) has been developed for the detection of cracks and buried pipes in urban grounds. It is made of two shielded Ultra Wide Band (UWB) bowtie-slot antennas operating in the frequency band [0.3;4] GHz. GPR signals contain not only responses of targets, but also unwanted effects from antenna coupling in air and in the soil, system ringing, and soil reflections that can mask the proper detection of useful information. Thus, it appears necessary to propose and assess several clutter reduction techniques as pre-processing techniques to improve the signal-to-noise ratio, discriminate overlapping responses issued from the targets and the clutter, and ease the use of data processing algorithms for target detection, identification or reconstruction. In this work, we have evaluated on Bscan profiles three different statistical data analysis such as mean subtraction, Principal Component Analysis (PCA), and Independent Component Analysis (ICA) considering a shallow and a medium depth target. The receiver operating characteristics (ROC) graph has allowed to evaluate the performance of each data processing in simulations and measurements to further draw a comparison in order to select the technique most adapted to a given soil structure with its radar probing system.