A New Method to Determine Evidence Distance

DS evidence theory is a hot topic in multi-sensor data fusion because of its advantages in multi-source information presentation and processing. Considering the difficulties of sensor reliability priority information acquisition, how to compute reliability of sensor via the evidence distance in multi-sensor data fusion system and how to select consistent evidence for combination via the evidence distance when many homogeneous sensors exist are the open issues. In this paper, a new evidence distance is presented in order to avoid the disadvantage of Diaz´s distance measure in the logical selection of tuning parameter and Jousselme´s distance measure in not considering the proximity of reference frame to the frame of discernment. This method firstly defines a fuzzy linguistic term set to express the proximity of reference frame to the frame of discernment, in which the semantics of the terms is given by a triangular membership function and a rewarding or penalizing function. Then similarity measurement matrix is achieved by weighting the rewarding or penalizing function using membership degree. Finally Jousselme´s distance framework is used to compute the evidence distance. Two numerical examples illustrate the efficiency of the method proposed.