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Proceedings Paper

Multisensor probabilistic fusion for mine detection
Author(s): Mark L. Yee
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Paper Abstract

In this paper, probabilistic fusion of multi-sensor data is applied to mine detection. Probabilistic fusion combines information in the form of scores from automatic target recognition (ATR) algorithms for each sensor. This fusion method has previously demonstrated improved mine detection performance when used with multi-sensor data from the Mine Hunter/Killer system. The sensor suite includes a ground- penetrating radar, metal detectors, and an IR camera; data were collected at a prepared test site. Results of applying the probabilistic fusion method to recent MH/K multi-sensor data using various new ATR algorithms are presented and analyzed in detail. Changes in detection performance are quantified for different combinations of the various ATR algorithms and sensors. It is shown that fusion improves mine detection performance even when the individual sensor and ATR algorithms have very different performance levels. This implies that multi-sensor approaches to mien detection should continue to be pursued.

Paper Details

Date Published: 18 October 2001
PDF: 11 pages
Proc. SPIE 4394, Detection and Remediation Technologies for Mines and Minelike Targets VI, (18 October 2001); doi: 10.1117/12.445424
Show Author Affiliations
Mark L. Yee, Sandia National Labs. (United States)

Published in SPIE Proceedings Vol. 4394:
Detection and Remediation Technologies for Mines and Minelike Targets VI
Abinash C. Dubey; James F. Harvey; J. Thomas Broach; Vivian George, Editor(s)

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