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.
Konduri et al. (Fri,) studied this question.