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Proceedings Paper

Optimization of the HSTAMIDS landmine detection algorithm through genetic algorithms
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Paper Abstract

CyTerra's dual sensor HSTAMIDS system has demonstrated promising landmine detection capabilities in extensive government-run field tests. Further optimization of the successful PentAD algorithm is desirable to maintain the high probability of detection (Pd) while lowering the false alarm rate (FAR). PentAD contains several input parameters, making such optimization using standard Monte-Carlo techniques too 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 further optimize the numerical values of the dual-sensor algorithm parameters in more practical time frames. Genetic algorithm techniques have also been applied to choose among several sub-models and fusion techniques to potentially train the HSTAMIDS system in new ways. An analysis of genetic algorithm results has indicated that ground type may have a significant impact on the optimal parameter set. In this presentation we discuss the performance of the resulting ground-type based genetic algorithm as applied to field data.

Paper Details

Date Published: 10 June 2005
PDF: 6 pages
Proc. SPIE 5794, Detection and Remediation Technologies for Mines and Minelike Targets X, (10 June 2005); doi: 10.1117/12.603665
Show Author Affiliations
Ravi Konduri, CyTerra Corp. (United States)
Geoff Solomon, CyTerra Corp. (United States)
Richard McCoy, CyTerra Corp. (United States)
Herbert Duvoisin, CyTerra Corp. (United States)
Elizabeth Bartosz, CyTerra Corp. (United States)


Published in SPIE Proceedings Vol. 5794:
Detection and Remediation Technologies for Mines and Minelike Targets X
Russell S. Harmon; J. Thomas Broach; John H. Holloway Jr., Editor(s)

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