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.
The Autonomous Mine Detection Sensor (AMDS) is a program to develop a suite of advanced sensor technology on a vehicular robot (PackBot). These sensors are characterized as having high detection performance and low false alarm rate. The AMDS program is sponsored by the U.S. Army CERDEC RDES Night Vision and Electronic Sensors Directorate (NVESD). The CyTerra Corporation solution to this problem is the combination of a Ground Penetration Radar (GPR) and a Metal Detector (MD) which comprise the AN/PSS-14. This paper presents the CyTerra Corporation concept and includes performance results from early government sponsored tests.
Full polarimetric turntable target signature data is used to develop Inverse Synthetic Aperture Radar (ISAR) plots to analyze high resolution target scatter centers. The data was collected using in-scene calibration reflectors to correct for transmit and receive polarization distortions in amplitude and phase. Scatter centers identified to have high radar cross section (RCS) with strong persistency are then analyzed in full polarimetric scatter matrix (PSM) space to discover uniqueness between target types. Target decomposition techniques are used to analyze the target centers. A unique scheme for isolating the effects of a desired scatter phenomenology is described in this paper with results shown for a truck target.
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