Real-time landmine detection with ground-penetrating radar using discriminative and adaptive hidden Markov models

Authors:
Hichem Frigui;K. C. Ho;Paul Gader
Affiliations:
Department of Computer Engineering and Computer Science, University of Louisville, Louisville, KY;Department of Electrical and Computer Engineering, University of Missouri-Columbia, Columbia, MO;Department of Computer and Information Science and Engineering, University of Florida, Gainesville, FL
Venue:
EURASIP Journal on Applied Signal Processing
Year:
2005

Citing 2
Cited 2

Digital signal processing (3rd ed.): principles, algorithms, and applications

Digital signal processing (3rd ed.): principles, algorithms, and applications
Fuzzy logic detection of landmines with ground penetrating radar

Signal Processing - Special issue on fuzzy logic in signal processing

Detection and discrimination of land mines in ground-penetrating radar based on edge histogram descriptors and a possibilistic K-nearest neighbor classifier

IEEE Transactions on Fuzzy Systems
An evaluation of several fusion algorithms for anti-tank landmine detection and discrimination

Information Fusion

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Abstract

We propose a real-time software system for landmine detection using ground-penetrating radar (GPR). The system includes an efficient and adaptive preprocessing component; a hidden Markov model-(HMM-) based detector; a corrective training component; and an incremental update of the background model. The preprocessing is based on frequency-domain processing and performs ground-level alignment and background removal. The HMM detector is an improvement of a previously proposed system (baseline). It includes additional pre-and postprocessing steps to improve the time efficiency and enable real-time application. The corrective training component is used to adjust the initial model parameters to minimize the number of misclassification sequences. This component could be used offline, or online through feedback to adapt an initial model to specific sites and environments. The background update component adjusts the parameters of the background model to adapt it to each lane during testing. The proposed software system is applied to data acquired from three outdoor test sites at different geographic locations, using a state-of-the-art array GPR prototype. The first collection was used as training, and the other two (contain data from more than 1200 m2 of simulated dirt and gravel roads) for testing. Our results indicate that, on average, the corrective training can improve the performance by about 10% for each site. For individual lanes, the performance gain can reach 50%.