Regional processing of GPR data in an imperfect world

Abstract

Designing robust landmine detection algorithms for ground penetrating radar (GPR) remains a challenging task due to variations of environmental conditions and diverse clutter objects in the soil, among others. The problem is aggravated for handheld systems by introducing operator motion and by the position uncertainty. Even though aggregating consecutive GPR samples to form multi-sample features seems to be an intuitively sensible approach to improve Pd/Pf, determining multi-sample features that are robust to the operator motion and position uncertainty is a formidable task. In this paper, we propose an ATR method to identify mines based on handheld GPR data collected for regional processing, where systematic operator motion is required and perhaps some position information is collected along with the data. The regional processing is intended to be conducted after other initial detection methods have identified an area for further interrogation. In this study, we will use GPR data that were collected by a robotic arm. In order for the developed ATR method to be applicable to data collected by human operators, which have greater position uncertainty, we focus on features that can still be used either directly or with minor modification when accurate sensor positions are not available. We tested two classes of classifiers, Support Vector Machines (SVM) and Gaussian Mixtures (GM). For both classifiers, less complex forms of the classifiers outperform those with more complicated structures. The reason is that the training set is relatively small compared to the diversity of the mines and the clutter objects in the training set.