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Proceedings Paper

Automatic classification of unexploded ordnance applied to Spencer Range live site for 5x5 TEMTADS sensor
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Paper Abstract

This paper details methods for automatic classification of Unexploded Ordnance (UXO) as applied to sensor data from the Spencer Range live site. The Spencer Range is a former military weapons range in Spencer, Tennessee. Electromagnetic Induction (EMI) sensing is carried out using the 5x5 Time-domain Electromagnetic Multi-sensor Towed Array Detection System (5x5 TEMTADS), which has 25 receivers and 25 co-located transmitters. Every transmitter is activated sequentially, each followed by measuring the magnetic field in all 25 receivers, from 100 microseconds to 25 milliseconds. From these data target extrinsic and intrinsic parameters are extracted using the Differential Evolution (DE) algorithm and the Ortho-Normalized Volume Magnetic Source (ONVMS) algorithms, respectively. Namely, the inversion provides x, y, and z locations and a time series of the total ONVMS principal eigenvalues, which are intrinsic properties of the objects. The eigenvalues are fit to a power-decay empirical model, the Pasion-Oldenburg model, providing 3 coefficients (k, b, and g) for each object. The objects are grouped geometrically into variably-sized clusters, in the k-b-g space, using clustering algorithms. Clusters matching a priori characteristics are identified as Targets of Interest (TOI), and larger clusters are automatically subclustered. Ground Truths (GT) at the center of each class are requested, and probability density functions are created for clusters that have centroid TOI using a Gaussian Mixture Model (GMM). The probability functions are applied to all remaining anomalies. All objects of UXO probability higher than a chosen threshold are placed in a ranked dig list. This prioritized list is scored and the results are demonstrated and analyzed.

Paper Details

Date Published: 7 June 2013
PDF: 8 pages
Proc. SPIE 8709, Detection and Sensing of Mines, Explosive Objects, and Obscured Targets XVIII, 870904 (7 June 2013); doi: 10.1117/12.2016118
Show Author Affiliations
John B. Sigman, Thayer School of Engineering at Dartmouth (United States)
Benjamin E. Barrowes, Thayer School of Engineering at Dartmouth (United States)
Kevin O'Neill, Thayer School of Engineering at Dartmouth (United States)
Fridon Shubitidze, Thayer School of Engineering at Dartmouth (United States)

Published in SPIE Proceedings Vol. 8709:
Detection and Sensing of Mines, Explosive Objects, and Obscured Targets XVIII
J. Thomas Broach; Jason C. Isaacs, Editor(s)

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