Physics-based Deformations of Ground Penetrating Radar Signals to Improve the Detection of Buried Explosives

摘要

A number of recent algorithms have shown improved performance in detecting buried explosive threats by statistically modeling target responses observed in ground penetrating radar (GPR) signals. These methods extract features from known examples of target responses to train a statistical classifier. The statistical classifiers are then used to identify targets emplaced in previously unseen conditions. Due to the variation in target GPR responses caused by factors such as differing soil conditions, classifiers require training on a large, varied dataset to encompass the signal variation expected in operational conditions. These training collections generally involve burying each target type in a number of soil conditions, at a number of burial depths. The cost associated with both burying the targets, and collecting the data is extremely high. Thus, the conditions and depths sampled cover only a subset of possible scenarios. The goal of this research is to improve the ability of a classifier to generalize to new conditions by deforming target responses in accordance with the physical properties of GPR signals. These signal deformations can simulate a target response under different conditions than those represented in the data collection. This research shows that improved detection performance in previously unseen conditions can be achieved by utilizing deformations, even when the training dataset is limited.