Optimization of the HSTAMIDS landmine detection algorithm through genetic algorithms

被引：0

作者：

Konduri, R ^{[1
]}

Solomon, G ^{[1
]}

McCoy, R ^{[1
]}

Duvoisin, H ^{[1
]}

Bartosz, E ^{[1
]}

机构：

[1] CyTerra Corp, Orlando, FL 32809 USA

来源：

Detection and Remediation Technologies for Mines and Minelike Targets X, Pts 1 and 2 | 2005年 / 5794卷

关键词：

D O I：

10.1117/12.603665

中图分类号：

T [工业技术];

学科分类号：

08 ;

摘要：

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 submodels 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.

引用

页码：1118 / 1123

页数：6