Proc. SPIE. 6217, Detection and Remediation Technologies for Mines and Minelike Targets XI
KEYWORDS: Detection and tracking algorithms, Sensors, Mining, Neural networks, Palladium, Algorithm development, Electromagnetic coupling, Land mines, Evolutionary algorithms, General packet radio service
The Autonomous Mine Detection Sensors (AMDS) program is developing a prototype autonomous mine-detection sensor suite designed to be mounted on a small robotic platform that can find buried anti-personnel mines. Over the past two years, CyTerra Corp. and NIITEK, Inc. have developed complementary senor suites using a variety of ground penetrating radar (GPR) and electromagnetic induction (EMI) sensor configurations. The AMDS program is also working with industry and academia to develop automatic target recognition (ATR) algorithms. This paper provides a brief overview of evaluations that have been performed at Army facilities. Probability of Detection (Pd) and Probability of False Alarm (Pfa) results are provided for signal-to-noise type detection algorithms and also for promising pattern classification and neural network algorithms that were developed by Duke University, the University of Missouri-Columbia, and the University of Florida. After an evaluation in October 2005, both contractors' sensors performed comparably (about 90% Pd and 40% Pfa) against low-metal anti-personnel mines at an Army test site seeded with typical clutter. In some cases, university-developed pattern classification and neural network algorithms have reduced the Pfa by a factor of two against these clutter sets.