Model based feature fusion approach

In recent years different sensor data fusion approaches have been analyzed and evaluated in the field of mine detection. In various studies comparisons have been made between different techniques. Although claims can be made for advantages for using certain techniques, until now there has been no single method identified with clearly outstanding performance in all scenarios. In this paper we describe a fusion approach based on a combination of modeling data and feature extraction. By using scenario and environmental data, model predictions are made of sensor data, performance data and mine feature data (shape, size, temperature contrast depth, etc.). These data are then compared with the sensor pre-processing as well as made available for use in the sensor fusion processing. These comparisons take into account the expected and measured sensor features for each object. In scenarios with sufficient a-priori knowledge it is expected that detection is improved by applying the model based feature fusion algorithm. The new concept is described and first results primarily based on thermal infrared test data recorded at the TNO test facility are presented. Particular attention was paid to the detection pre-processing and the feature fusion stage