Accession Number:

ADA364744

Title:

A Feasibility Study on Using Physics-Based Modeler Outputs to Train Probabilistic Neural Networks for UXO Classification

Descriptive Note:

Final rept.

Corporate Author:

NAVAL RESEARCH LAB WASHINGTON DC CHEMISTRY DIV

Personal Author(s):

• Shaffer, R. E.
• Rose-Pehrsson, S. L.
• McDonald, J. R.
• Hart, S. J.

Report Date:

1999-04-29

Pagination or Media Count:

46.0

Abstract:

A probabilistic neural network PNN has been applied to the detection and classification of unexploded ordnance UXO measured using magnetometry data collected using the Multi-sensor Towed Array Detection System MTADS. Physical parameters obtained from a physics based modeler were used to describe the UXO and scrap targets found at three sites Badlands Bombing Range BBR Target 1 and 2 and the Former Buckley Field. The PNN was trained and tested using cross validation CV software developed at NRL. The PNN was able to correctly identify between 84 to 94 of the targets. By adjusting the probability threshold, further improvements in the discrimination of UXO were possible 96 of the UXO were correctly identified for BBR Target 1, 100 for BBR Target 2, and 94 for the former Buckley Field. The ability to train using one site BBR target 2 and predict another BBR Target 1 was successful with 95 of the UXO correctly identified and a false alarm rate of 35.

Descriptors:

• *NEURAL NETS
• *EXPLOSIVES DETECTION
• *UNEXPLODED AMMUNITION
• SOFTWARE ENGINEERING
• PATTERN RECOGNITION
• TARGET CLASSIFICATION
• MAGNETOMETRY

Subject Categories:

• Cybernetics
• Miscellaneous Detection and Detectors

Distribution Statement:

APPROVED FOR PUBLIC RELEASE