Fusion of ground-penetrating radar and electromagnetic induction sensors for landmine detection and discrimination

Abstract

Ground penetrating radar (GPR) and electromagnetic induction (EMI) sensors provide complementary capabilities in detecting buried targets such as landmines, suggesting that the fusion of GPR and EMI modalities may provide improved detection performance over that obtained using only a single modality. This paper considers both pre-screening and the discrimination of landmines from non-landmine objects using real landmine data collected from a U.S. government test site as part of the Autonomous Mine Detection System (AMDS) landmine program. GPR and EMI pre-screeners are first reviewed and then a fusion pre-screener is presented that combines the GPR and EMI prescreeners using a distance-based likelihood ratio test (DLRT) classifier to produce a fused confidence for each pre-screener alarm. The fused pre-screener is demonstrated to provide substantially improved performance over the individual GPR and EMI pre-screeners. The discrimination of landmines from non-landmine objects using feature-based classifiers is also considered. The GPR feature utilized is a pre-processed, spatially filtered normalized energy metric. Features used for the EMI sensor include model-based features generated from the AETC model and a dipole model as well as features from a matched subspace detector. The EMI and GPR features are then fused using a random forest classifier. The fused classifier performance is superior to the performance of classifiers using GPR or EMI features alone, again indicating that performance improvements may be obtained through the fusion of GPR and EMI sensors. The performance improvements obtained both for pre-screening and for discrimination have been verified by blind test results scored by an independent U.S. government contractor.