This paper explores the concepts of multisensor data fusion based on Dempster-Shafer (DS) evidential theory in order to achieve a mine versus false-alarm (FA) classification of landmine targets. Initially, a decision-level DS algorithm is proposed to combine the evidence from multiple sensors of the landmine detection system developed by the General Dynamics Canada Limited (GD Canada). Subsequently, a feature-level DS fusion algorithm is employed to operate on a set of features reported by the ground penetrating radar (GPR) sensor of the system. The data used in the present study was acquired from the Aberdeen Proving Grounds (APG) test site in USA as part of the Ground Standoff Mine Detection System (GSTAMIDS) trials. The proposed decision-level DS algorithm yielded a probability of detection (PD) of 92.53% at a false-alarm rate (FAR) value of 0.0697 FAs/m2. The PD and FAR performance results achieved by using the decision-level DS algorithm are comparable with the results obtained using three other decision-level fusion algorithms that were previously developed by GD Canada based on heuristic, Bayesian inference, and voting fusion concepts. On the other hand, feature-level DS fusion, when tested with information presented by the GPR sensor only, resulted in a higher PD value of 78.54% as compared to the corresponding result of 61.43% obtained using the heuristic algorithm. The GPR sensor is one of the three scanning sensors present in the system.
The application of Dempster-Shafer theory for landmine detection
Architectures, Algorithms, and Applications, Multisensor, Multisource Information Fusion, 2003 ; 103-112
2003
10 Seiten, 9 Quellen
Aufsatz (Konferenz)
Englisch
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