An Evidence Theory Based Multi Sensor Data Fusion for Multiclass Classification.

Multi-sensor data fusion is widely used in various application domains. Integration of multiple sensors is a complex problem. This is because it is often characterized by uncertainty due to randomness and non-specificity. The Dempster Shafer (DS) theory of evidence has often been used for modelling and reasoning under uncertainty. However, the DS rule of combination is often prone to counter-intuitive results when combining pieces of evidence that are highly conflicting. As a result, several alternative combination rules have emerged. One approach is to assign weight to each basic probability assignment (BPA) prior to the use of the DS rule of combination. Most existing methods of assigning weight only focus on the credibility of each BPA without considering the reliability of the source of the BPA. In this work, we propose a multi-sensor data fusion that takes into consideration both the reliability of each BPA source and the credibility degree. A benchmark dataset was used to evaluate the effectiveness of the proposed method. To further assess the robustness of the proposed method in handling uncertainty, different noise levels were introduced to the training set.