Enhanced detection of landmines using broadband EMI data

EMI sensors are used extensively to detect landmines. These sensors operate by detecting the metal that is present in mines. However, mines vary in their construction from metal-cased varieties with a large mass of metal to plastic-cased varieties with minute amounts of metal. Unfortunately, there is often a significant amount of metallic clutter present in the environment. Consequently, EMI sensors that utilize traditional detection algorithms based solely on metal content suffer from large false alarm rates. This false alarm problem can be mitigated for high-metal content mines by developing statistical algorithms that exploit models of the underlying physics. In such models it is commonly assumed that the soil has a negligible effect on the sensor response. To date, no testing has been done to validate the assumption that when modeling the response of EMI sensors to low-metal mines, the soil effects are negligible. In addition, advanced algorithms have not been applied specifically to the detection of low-metal names. The Joint UXO Coordination Office (JUXOCO) is sponsoring a series of experiments designed to establish a performance baseline for EMI sensors. This baseline will be used to measure the potential improvements in performance offered by advanced signal processing algorithms. This paper describes the results of several experiments performed in conjunction with the JUXOCO effort. The results indicate that (1) the properties of the soil do affect the response of a broadband EMI sensor to low-metal mines, and (2) advanced algorithms can improve detection performance over traditional algorithms based solely on metal content.

Duke Scholars

Author Leslie M. Collins Electrical and Computer Engineering