Landmine detection with ground penetrating radar using hidden Markov models

Novel, general methods for detecting landmine signatures in ground penetrating radar (GPR) using hidden Markov models (HMMs) are proposed and evaluated. The methods are evaluated on real data collected by a GPR mounted on a moving vehicle at three different geographical locations. A large library of...

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Bibliographic Details
Published in:IEEE transactions on geoscience and remote sensing 2001-06, Vol.39 (6), p.1231-1244
Main Authors: Gader, P.D., Mystkowski, M., Yunxin Zhao
Format: Article
Language:English
Subjects:
Algorithms
Clutter
Computer simulation
Construction
Decision making
Detectors
Feature extraction
Ground penetrating radar
Hidden Markov models
Land mines
Landmine detection
Mathematical models
Radar detection
Signatures
Testing
Vehicles
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Summary:Novel, general methods for detecting landmine signatures in ground penetrating radar (GPR) using hidden Markov models (HMMs) are proposed and evaluated. The methods are evaluated on real data collected by a GPR mounted on a moving vehicle at three different geographical locations. A large library of digital GPR signatures of both landmines and clutter/background was constructed and used for training. Simple, but effective, observation vector representations are constructed to naturally model the time-varying signatures produced by the interaction of the GPR and the landmines as the vehicle moves. The number and definition of the states of the HMMs are based on qualitative signature models. The model parameters are optimized using the Baum-Welch algorithm. The models were trained on landmine and background/clutter signatures from one geographical location and successfully tested at two different locations. The data used in the test were acquired from over 6000 m/sup 2/ of simulated dirt and gravel roads, and also off-road conditions. These data contained approximately 300 landmine signatures, over half of which were plastic-cased or completely nonmetal.
ISSN:0196-2892
1558-0644
DOI:10.1109/36.927446