Bayesian sensor fusion for land-mine detection using a dual-sensor hand-held device

This work presents a methodology and practical implementation of sensor fusion for land-mine detection using a novel multi-sensor hand-held device composed by a triple coil metal detector and a gas sensor. The proposed approach consists on merging data from both sensors in order to reduce the false alarm rate, particularly by using odor information. A Bayesian approach is proposed for the sensor fusion. Results show a false alarm rate of 1.4 to 1, a mine detection rate of 100% and a mine localization mean absolute error of 3 cm. Furthermore the resulting mine presence probability distribution maps represent an important visualization tool for mine clearance hand-held device users.

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