<title>Detection and identification of mines from natural magnetic and electromagnetic resonances</title>

Abstract

The detection of land mines has two fundamental goals: the first is a high detection rate (low probability of missing a mine) and the second is a low false alarm rate. Detection of mines and mine-like objects is generally not difficult; the problem is the high false-alarm rate caused by detection of innocuous objects such as shrapnel or metal junk, or even rocks or voids in the soil. The problem is one of discrimination, not one of detection. In order to maximize the success of achieving this goal, a mine detector needs to incorporate many complementary sensor technologies and to utilize the concept of sensor data fusion. Two subsystems employ new signal processing techniques which extract certain features from the data that are unique identifiers on the mines. These features are the natural magentic and electromagnetic resonances, which form the impulse response function, or equivalently, the natural frequencies represented by poles in the complex frequency plane. For different objects these are sufficiently distinct that pattern recognition processes can be used to arrive at a probability of a match to a particular mine.