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Proceedings Paper

Multisensor neural network approach to mine detection
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Paper 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.

Paper Details

Date Published: 18 October 2001
PDF: 8 pages
Proc. SPIE 4394, Detection and Remediation Technologies for Mines and Minelike Targets VI, (18 October 2001); doi: 10.1117/12.445429
Show Author Affiliations
Amber L. Iler, Veridian Systems, Inc. (United States)
Jay A. Marble, Veridian Systems, Inc. (United States)
Patrick J. Rauss, Army Research Lab. (United States)

Published in SPIE Proceedings Vol. 4394:
Detection and Remediation Technologies for Mines and Minelike Targets VI
Abinash C. Dubey; James F. Harvey; J. Thomas Broach; Vivian George, Editor(s)

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