Predicting GPR target locations using time delay differences

Abstract

We describe an efficient approach for finding probable target areas quickly with a minimal number of Ground Penetrating Radar (GPR) measurements. Since a potential GPR target creates a hyperbolic signature in the space-time domain, our approach uses the time delay differences from consecutive GPR A-Scan data to estimate the location of the apex of the hyperbolic signature, thus locating a target. This apex prediction method uses many fewer measurements than a full backprojection algorithm. Regions of low target probability are determined using a Neyman-Pearson detection approach in order to eliminate redundant measurements. In this regard, our approach is especially suitable as a pre-screener: other sensors that are more accurate, but require more measurement time, can then be applied only to high probability-of-target areas to corroborate results, differentiate between targets, or provide more accurate location measurements. Compared to a standard backprojection algorithm more signal-to-noise ratio (SNR) is needed to achieve similar detection performance. This SNR loss can be reduced by using a more conservative algorithm which reduces the step size of the GPR antenna. Results from experimental data collected at a model mine field at the Georgia Institute of Technology show that target positions can be found accurately using less than 10 % of the measurements utilized by conventional imaging algorithms.