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Proceedings Paper

Buried landmine detection using multivariate normal clustering
Author(s): Brian M. Duston
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Paper Abstract

A Bayesian classification algorithm is presented for discriminating buried land mines from buried and surface clutter in Ground Penetrating Radar (GPR) signals. This algorithm is based on multivariate normal (MVN) clustering, where feature vectors are used to identify populations (clusters) of mines and clutter objects. The features are extracted from two-dimensional images created from ground penetrating radar scans. MVN clustering is used to determine the number of clusters in the data and to create probability density models for target and clutter populations, producing the MVN clustering classifier (MVNCC). The Bayesian Information Criteria (BIC) is used to evaluate each model to determine the number of clusters in the data. An extension of the MVNCC allows the model to adapt to local clutter distributions by treating each of the MVN cluster components as a Poisson process and adaptively estimating the intensity parameters. The algorithm is developed using data collected by the Mine Hunter/Killer Close-In Detector (MH/K CID) at prepared mine lanes. The Mine Hunter/Killer is a prototype mine detecting and neutralizing vehicle developed for the U.S. Army to clear roads of anti-tank mines.

Paper Details

Date Published: 22 October 2001
PDF: 10 pages
Proc. SPIE 4379, Automatic Target Recognition XI, (22 October 2001); doi: 10.1117/12.445397
Show Author Affiliations
Brian M. Duston, BAE Systems Mission Solutions (United States)

Published in SPIE Proceedings Vol. 4379:
Automatic Target Recognition XI
Firooz A. Sadjadi, Editor(s)

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