The detection ofland 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 ofsensor data fusion. Two subsystems employ new signal processing techniques which extract certain features from the data that are unique identifiers ofthe mines. These features are the natural magnetic 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 ofa match to a particular mine.
The measurement of electromagnetic fields and related quantities in a lightning environment is a challenging problem, especially at high frequencies and/or in the immediate vicinity of the lightning arcs and corona. This paper reviews the techniques for accomplishing such measurements in these regimes with examples. These sensors are often the same as for the nuclear electromagnetic pulse (EMP), but significant differences also appear.
Metal land mines still account for a large percentage of land mines, even with the advent of the so-called plastic mines. The metal detector thus remains a viable tool in the mine detector's bag. The limitation of the metal detector is not in detection of the mines, but in the additional detection of metal clutter. A metal detector has been developed which can largely discriminate the mines from the clutter, thereby greatly reducing false alarm rates. This "mine detector" is designed to characterize the magnetic polarizability dyadic of the metal objects, and to use pattern recognition to determine the goodness-of-fit to the responses of known mines. Data are presented from test runs conducted for the US Army for buried metal mines. Data are also presented for some non-mine metal targets. The characterization of the mines as threats is performed in a totally autonomous system, with high probability-of-detection and low false alarm rate. We can also generally tell one mine type from another.
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