Dipole Models for UXO Discrimination at Live Sites
This project will demonstrate advanced methods of analyzing next generation sensor data for the detection and discrimination of unexploded ordnance (UXO) in a production setting as part of the live site demonstrations. The team from Sky Research and the University of British Columbia (UBC) will further develop and test a suite of discrimination strategies at increasingly complex and cluttered sites for a wide range of electromagnetic induction (EMI) sensors, develop work flows and processing tools for the robust implementation of advanced discrimination and classification methodologies in the production environment, and transition data processing expertise and technology to production teams through training and work sessions with algorithm and software developers. The aim is to provide flexible, reliable, robust, efficient, and effective signal processing algorithms and strategies that can be adapted to (virtually) any site conditions or EMI sensor technology.
Advances in algorithmic and methodological techniques that involve work on magnetic soils, robust statistics, practical discrimination strategies, and model selection, achieved with prior SERDP support, will be leveraged in this demonstration. The analysis is rooted in a dipole inversion model and in recovering a set of parameters, comprised of an object's location and polarization tensor. These parameters are then compared to a physics-based model of the object being interrogated. Once the parameters are recovered by inversion, a subset of the parameters is used to form feature vectors to guide either a statistical or rule-based classifier.
Points of Contact
Dr. Leonard Pasion
Black Tusk Geophysics, Inc.
SERDP and ESTCP
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