Land Mine and Clutter Object Discrimination Using Wavelet and Time Domain Spatially Distributed Features from Metal Detectors and Their Fusion with GPR Features for Hand-Held Units

Abstract

This paper presents some advances in discrimination and fusion algorithms using metal detector (MD) and ground penetrating radar (GPR) sensors in a robotic wand unit. Previously investigated spatially distributed features are extended and fused with discrete wavelet transform representations of MD data. A multilayer perceptron technique is then applied to discriminate between land mine and land mine-like objects based on the wavelet coefficient and time domain features separately. Using MD wavelet and time domain fusion, the probability of false alarms is reduced by 46.0% and 18.0% over the wavelet and time domain models, respectively, at 0.95 probability of detection. Fusion results are presented for the MD and GPR sensors to demonstrate that the two sensors provide complementary information for significantly reducing the probability of false alarm. Blind test results from a government test facility are presented to evaluate the effectiveness of the algorithms.