Sensor fusion of EMI and GPR data for improved landmine detection

It is widely accepted that single sensors cannot simultaneously achieve both high detection rates and low false alarm rates for the landmine detection problem. Thus, in this paper we consider the fusion of two types of sensors, electromagnetic induction (EMI) and ground penetrating radar (GPR). In its most common instantiation, EMI essentially provides metal detection and thus detects mines with high metal content as well as metal debris in the environment. More advanced EMI systems have begun to show potential for discriminating such debris from landmines. GPR is also used for landmine detection since it can detect and identify low-metallic subsurface anomalies. In our previous work, we have shown that a Bayesian detection approach can be applied to EMI and GPR data and provide improvements in false alarm rates. In this paper, we present results that indicate that statistical signal processing techniques can be applied simultaneously to GPR and EMI data and that reductions in false alarm rates can be achieved. We present results for two landmine detection systems, both handheld, and when possible compare the results to those obtained by a human operator who essentially fuses the outputs of the single sensor systems.

Duke Scholars

Author Leslie M. Collins Electrical and Computer Engineering

Author Lisa Gresham Huettel Electrical and Computer Engineering

Author Stacy L. Tantum Electrical and Computer Engineering