A comparison of decision-level sensor-fusion methods for anti-personnel landmine detection

We present the sensor-fusion results obtained from measurements within the European research project ground explosive ordinance detection (GEODE) system that strives for the realisation of a vehicle-mounted, multi-sensor, anti-personnel landmine-detection system for humanitarian de-mining. The system has three sensor types: a metal detector (MD), an infrared camera (IR), and a ground penetrating radar (GPR). The output of the sensors is processed to produce con®dence levels on a grid covering the test-bed. A con®dence level expresses a con®dence or belief in a landmine detection on a certain position. The grid with con®dence levels is the input for the decision-level sensor-fusion and provides a co-registration of the sensors. The applied fusion methods are naive Bayes' approaches, Dempster±Shafer theory, fuzzy probabilities, a rule-based method, and voting techniques. To compare fusion methods and to analyse the capacity of a method to separate landmines from the background on the basis of the output of di€erent sensors, we provide an analysis of the di€erent methods by viewing them as discriminant functions in the sensor con®dence space. The results of experiments on real sensor data are evaluated with the leave-one-out method.