Sensor fusion in anti-personnel mine detection using a two-level belief function model

A two-level approach for modeling and fusion of anti-personnel mine detection sensors in terms of belief functions within the Dempster-Shafer framework is presented. Three promising and complementary sensors are considered: a metal detector, an infrared camera, and a ground-penetrating radar. Since the metal detector, the most often used mine detection sensor, provides measures that have different behaviors depending on the metal content of the observed object, the first level aims at identifying this content and at providing a classification into three classes. Depending on the metal content, the object is further analyzed at the second level toward deciding the final object identity. This process can be applied to any problem where one piece of information induces different reasoning schemes depending on its value. A way to include influence of various factors on sensors in the model is also presented, as well as a possibility that not all sensors refer to the same object. An original decision rule adapted to this type of application is proposed, as well as a way for estimating confidence degrees. More generally, this decision rule can be used in any situation where the different types of errors do not have the same importance. Some examples of obtained results are shown on synthetic data mimicking reality and with increasing complexity. Finally, applications on real data show promising results.