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Proceedings Paper

Genetic optimization of the HSTAMIDS landmine detection algorithm
Author(s): Ravi K. Konduri; Geoff Z. Solomon; Keith DeJong; Herbert A. Duvoisin; Elizabeth E. Bartosz
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Paper Abstract

CyTerra's dual sensor HSTAMIDS system has demonstrated exceptional landmine detection capabilities in extensive government-run field tests. Further optimization of the highly successful PentAD-class algorithms for Humanitarian Demining (HD) use (to enhance detection (Pd) and to lower the false alarm rate (FAR)) may be possible. PentAD contains several input parameters, making such optimization computationally intensive. Genetic algorithm techniques, which formerly provided substantial improvement in the detection performance of the metal detector sensor algorithm alone, have been applied to optimize the numerical values of the dual-sensor algorithm parameters. Genetic algorithm techniques have also been applied to choose among several sub-models and fusion techniques to potentially train the HSTAMIDS HD system in new ways. In this presentation we discuss the performance of the resulting algorithm as applied to field data.

Paper Details

Date Published: 21 September 2004
PDF: 9 pages
Proc. SPIE 5415, Detection and Remediation Technologies for Mines and Minelike Targets IX, (21 September 2004); doi: 10.1117/12.544318
Show Author Affiliations
Ravi K. Konduri, CyTerra Corp. (United States)
Geoff Z. Solomon, CyTerra Corp. (United States)
Keith DeJong, CyTerra Corp. (United States)
Herbert A. Duvoisin, CyTerra Corp. (United States)
Elizabeth E. Bartosz, CyTerra Corp. (United States)

Published in SPIE Proceedings Vol. 5415:
Detection and Remediation Technologies for Mines and Minelike Targets IX
Russell S. Harmon; J. Thomas Broach; John H. Holloway Jr., Editor(s)

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