Previous results with Hidden Markov models showed that they could be used to perform reliable classification between mines and background/clutter under a variety ofconditions. Since then, new features have been defined and continuous models have been implemented. In this paper, new results are presented for applying them to calibration lane GPR data obtained during the Vehicle Mounted Mine Detection (VMMD) Advanced Technology Demonstrations. Morphological Neural Networks can be trained to perform feature extraction and detection simultaneously. Generalizing these networks to incorporate Choquet Integrals provides the added capability ofrobustness and improved feature learning. These features can provide complementary information compared to those generated by humans. Results of applying these networks to calibration lane GPR data from the VMMD Advanced Technology Demonstrations are provided. Combinations of the various methodologies with previously developed algorithms are also evaluated.
Previous experiments with Hidden Markov Models showed that they could be used as an algorithm for landmine detection. In this paper we propose a basic adaptive algorithm for discrete Hidden Markov Model for landmine detection. The performance of adaptive HMM is investigated using GPR data from gathered during the Vehicle Mounted Mine Detection Advanced Technology Demonstrations. In both cases the adaptive HMM outperforms the baseline HMM model, trained offline using Baum-Welch algorithm.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.