Time-domain metal detector and GPR data processing and fusion for land mine detection

Abstract

ABSTRACT For the landmine detection problem, a detector that provides a high probability of detection and a lowprobability of false alarm is needed. It is often the case that detectors satisfy one requirement at the cost ofpoor performance with regard to the other. Single sensors cannot achieve this goal, since every sensor hasits advantages and disadvantages when dealing with a large variety of landmines, from large metal-casedmines to small plastic-cased mines, etc. Thus, in this paper we consider two types of sensors, EMI andGPR. Time-domain EMI has been extensively used in the military and humanitarian demining. However, itis essentially a metal detector, thus, can detect mines with high metal content successfully, as well as metaldebris in the environment. This yields poor detection performance on mines with low metal content andhigh false alarm rate ifthe field was contaminated by metallic clutter. On the other hand, GPR is a potentialtool for landmine detection, since it can detect and identify subsurface anomalies. A GPR system with widefrequency band can achieve good resolution and adequately deep penetration for landmine detection. In ourprevious work, we have shown that Bayesian detection approach can be applied to EMI data and providepromising results. In this paper, we present results that indicate that statistical signal processing techniquescan improve performance over the conventional detection methods, which are usually based on the energypresent in the signal. Specifically, we consider data taken by the Coleman Research Corporation (CRC)Handheld Standoff Mine Detection System (HSTAMIDS) at Fort A. P. Hill, VA. and Yuma, AZ. Theactive system of the HSTAMIDS contains co-located metal detector (MD) and GPR sensors, which allowsus to fuse the data from the MD and GPR sensors. Thus, in addition to discussing individual sensor dataprocessing, we also present result of data fusion of both the MD and the GPR data using the HSTAMIDSsystem.