Improved Dempster-Shafer Sensor Fusion using Distance Function and Evidence Weighted Penalty: Application in Object Detection

Abstract

Dempster-Shafer (DS) combination method can deal with the uncertainty and inconsistency of multi-sensor data fusion and widely used in data fusion, fault detection, pattern recognition, and supplier selection. The original DS theory has limitations such as its inability to handle conflicting data properly which can result into inaccuracy in the output of a multi-sensor data fusion process. To eliminate such limitations of the original DS theory, a novel method is proposed in this paper that uses distance function to measure the credibility of each sensor and uses weighted penalty of faulty sensor evidence to create maximum evidence for the correct detection. A detailed example for object detection with conflicting sensor input is presented which showcases all the steps of the proposed method. A numerical simulation is used to show that the proposed method effectively eliminates the limitations of original DS combination rule and offers an improvement over the current state-of-the-art models.