Robust Machine Learning for UXO Detection

Award Information
Agency: Department of Defense
Branch: Army
Contract: W912HZ-06-C-0040
Agency Tracking Number: A032-2682
Amount: $729,949.00
Phase: Phase II
Program: SBIR
Awards Year: 2006
Solicitation Year: 2003
Solicitation Topic Code: A03-127
Solicitation Number: 2003.2
Small Business Information
500 West Cummings Park - Ste 3000, Woburn, MA, 01801
DUNS: 859244204
HUBZone Owned: N
Woman Owned: N
Socially and Economically Disadvantaged: Y
Principal Investigator
 Ssu-Hsin Yu
 Group Leader
 (781) 933-5355
Business Contact
 Robert Simpson
Title: Mgr of Fin/Controller
Phone: (781) 933-5355
Research Institution
Many of the areas designated for Base Realignment and Closure (BRAC) and Formerly Used Defense Sites (FUDS) are contaminated with Unexploded Ordnance (UXO) that needs to be cleared of before being returned to civilian use. Despite the significant research and development efforts, there is still plenty of room for improving the UXO detection technologies. For EMI and GPR sensors, the predominantly metallic UXO poses less of a problem for detection. The limiting factor is the high false alarms caused by metallic clutter often prevalent at those survey sites that add considerably to the clean-up cost due to the excavation. To mitigate the false alarms, many signal processing methods have been proposed that adapt the sensor signal to local environment, and/or impose UXO specific features to better differentiate UXO from clutter objects. Although many of the methods show good potential in a controlled environment, their actual performances in more realistic field tests often degrade, sometimes significantly, from those achieved in the laboratory settings due to the variability and diversity of UXO and the uncertainty in the UXO locations. In this effort, we propose several approaches for object feature extraction, feature selection, and classification of the detected objects. The main theme of our proposed approaches is to ensure robustness of the resultant UXO classification results under conditions that are not exactly the same as those assumed by the simplified target models or those of the training data.

* Information listed above is at the time of submission. *

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