FUZZY SENSOR FUSION BASED ON EVIDENCE THEORY AND ITS APPLICATION

Abstract

In multisensor systems, complementary observations from different sensors need to be combined with each other. Due to the uncertainty, sensor reports can be represented by fuzzy sets in order to efficiently deal with signal processing. In this article, a methodology to combine sensor reports in fuzzy environments based on Dempster–Shafer evidence theory is proposed. The basic probability assignment function is constructed by means of member functions. The numerical example on object recognition of a robot arm is shown to illustrate the efficiency of the presented approach.