Translator Disclaimer
18 October 2001 Multisensor probabilistic fusion for mine detection
Author Affiliations +
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.
© (2001) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Mark L. Yee "Multisensor probabilistic fusion for mine detection", Proc. SPIE 4394, Detection and Remediation Technologies for Mines and Minelike Targets VI, (18 October 2001);

Back to Top