Multi-sensor and Multi-platform Data Fusion for Buried Objects Detection and Localization

This paper describes a multi-sensor data-fusion approach to detect and localize landmines and unexploded ordnances (UXO) in a field using multiple mobile sensor carriers covering the area of interest. A procedure for high accuracy geo-referencing the field-data acquired by multiple platforms is proposed. Later, the geo-referenced sensor data is fused by a multi-step algorithm consisting on the detection of heterogeneities corresponding to objects followed by the classification of those objects through a decision-level fusion. The proposed approach was tested and validated in a military field containing more than twenty different objects buried on the ground, using two different metal detector (MD) arrays and one ground penetrating radar (GPR) array. Although the preliminary results can be considered encouraging, since it was possible to detect and classify all the buried objects, further tests are necessary in terms of classification, since the classification algorithm was trained with data collected previously from the same soil.