Multisensor neural network approach to mine detection

Amber L. Iler; Jay A. Marble; Patrick J. Rauss

doi:10.1117/12.445429

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18 October 2001 Multisensor neural network approach to mine detection

Amber L. Iler, Jay A. Marble, Patrick J. Rauss

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Proceedings Volume 4394, Detection and Remediation Technologies for Mines and Minelike Targets VI; (2001) https://doi.org/10.1117/12.445429
Event: Aerospace/Defense Sensing, Simulation, and Controls, 2001, Orlando, FL, United States

Abstract

A neural network is applied to data collected by the close-in detector for the Mine Hunter Killer (MHK) project with promising results. We use the ground penetrating radar (GPR) and metal detector to create three channels (two from the GPR) and train a basic, two layer (single hidden layer), feed-forward neural network. By experimenting with the number of hidden nodes and training goals, we were able to surpass the performance of the single sensors when we fused the three channels via our neural network and applied the trained net to different data. The fused sensors exceeded the best single sensor performance above 95 percent detection by providing a lower, but still high, false alarm rate. And though our three channel neural net worked best, we saw an increase in performance with fewer than three channels, as well.

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Amber L. Iler, Jay A. Marble, and Patrick J. Rauss "Multisensor neural network approach to mine detection", Proc. SPIE 4394, Detection and Remediation Technologies for Mines and Minelike Targets VI, (18 October 2001); https://doi.org/10.1117/12.445429

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