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Proceedings Paper

EMPACT 3D: an advanced EMI discrimination sensor for CONUS and OCONUS applications
Author(s): Joe Keranen; Jonathan S. Miller; Gregory Schultz; Morgan Sander-Olhoeft; Stephen Laudato
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Paper Abstract

We recently developed a new, man-portable, electromagnetic induction (EMI) sensor designed to detect and classify small, unexploded sub-munitions and discriminate them from non-hazardous debris. The ability to distinguish innocuous metal clutter from potentially hazardous unexploded ordnance (UXO) and other explosive remnants of war (ERW) before excavation can significantly accelerate land reclamation efforts by eliminating time spent removing harmless scrap metal. The EMI sensor employs a multi-axis transmitter and receiver configuration to produce data sufficient for anomaly discrimination. A real-time data inversion routine produces intrinsic and extrinsic anomaly features describing the polarizability, location, and orientation of the anomaly under test. We discuss data acquisition and post-processing software development, and results from laboratory and field tests demonstrating the discrimination capability of the system. Data acquisition and real-time processing emphasize ease-of-use, quality control (QC), and display of discrimination results. Integration of the QC and discrimination methods into the data acquisition software reduces the time required between sensor data collection and the final anomaly discrimination result. The system supports multiple concepts of operations (CONOPs) including: 1) a non-GPS cued configuration in which detected anomalies are discriminated and excavated immediately following the anomaly survey; 2) GPS integration to survey multiple anomalies to produce a prioritized dig list with global anomaly locations; and 3) a dynamic mapping configuration supporting detection followed by discrimination and excavation of targets of interest.

Paper Details

Date Published: 30 April 2018
PDF: 12 pages
Proc. SPIE 10628, Detection and Sensing of Mines, Explosive Objects, and Obscured Targets XXIII, 106280S (30 April 2018); doi: 10.1117/12.2306945
Show Author Affiliations
Joe Keranen, White River Technologies, Inc. (United States)
Jonathan S. Miller, White River Technologies, Inc. (United States)
Gregory Schultz, White River Technologies, Inc. (United States)
Morgan Sander-Olhoeft, White River Technologies, Inc. (United States)
Stephen Laudato, U.S. Army Night Vision & Electronic Sensors Directorate (United States)

Published in SPIE Proceedings Vol. 10628:
Detection and Sensing of Mines, Explosive Objects, and Obscured Targets XXIII
Steven S. Bishop; Jason C. Isaacs, Editor(s)

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