Advanced EMI Models and Classification Algorithms: The Next Level of Sophistication to Improve Discrimination of Challenging Targets



There are approximately eleven million acres of land at Department of Defense (DoD) and Department of Energy sites that are highly contaminated with unexploded ordnance (UXO). The well-known high cost associated with excavating all geophysical anomalies is one of the greatest impediments to the efficient cleanup of UXO-contaminated lands. Recent live-site discrimination studies at the former Camp San Luis Obispo (SLO), CA and the former Camp Butner, NC have revealed that advanced electromagnetic induction (EMI) sensors, which currently feature multi-axis illumination of targets and tri-axial vector sensing (e.g., MetalMapper) or exploit multistatic array data acquisition (e.g., TEMTADS), together with advanced EMI models, provide superb classification performance relative to the previous generation of single-axis monostatic sensors. However, these advances have yet to improve significantly the ability to classify small targets (i.e., with calibers ranging from 20 to 60 mm) or deep targets or provide the high fidelity necessary to distinguish overlapping target signatures in highly cluttered environments. In order to achieve small- and deep-target detection and classification with 100% confidence, it is therefore necessary to take to the next level of complexity the advanced models and new-generation-sensor deployment modalities that have so far proved successful.

The objective of this project is to improve the detection and classification of small and deep targets by investigating and developing fast, noise-tolerant EMI data pre-processing and inversion approaches and extending the detection range and spatial resolution of next-generation EMI systems by using different combinations of transmitter coils adjustable in both direction and current amplitude.

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Technical Approach

This research is aimed at developing efficient methodologies to improve EMI data collection and increase the signal-to-noise ratio (SNR) in order to allow discrimination of small and deep targets and to apply adaptive advanced modeling and hardware tools to next-generation sensors in order to enhance the classification of UXO targets. In particular, the project team will update the TEMTADS electronics and software and use the advanced EMI models including the Joint Diagonalization and the Ortho-Normalized Volume Magnetic Source model to enhance the detection and classification of small and deep targets at actual live-UXO sites.

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This project aims to improve technology that may lead to significant progress in detection and discrimination of small, deep, and overlapping targets, and maximize the information content of the data provided by advanced EMI sensors. A critical limiting factor for characterization is the ability to recover the location, orientation, and size of UXO with high accuracy. The project team contends that rich information and flexibility are available from, but not yet exploited by, current EMI systems and the team plans to explore this potentially rich pathway. (Anticipated Project Completion - 2015)

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Points of Contact

Principal Investigator

Dr. Fridon Shubitidze

Dartmouth College

Phone: 603-646-3671

Program Manager

Munitions Response


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