An improved belief entropy–based uncertainty management approach for sensor data fusion

Abstract

In real applications, sensors may work in complicated environments; thus, how to measure the uncertain degree of sensor reports before applying sensor data fusion is a big challenge. To address this issue, an improved belief entropy–based uncertainty management approach for sensor data fusion is proposed in this article. First, the sensor report is modeled as the body of evidence in Dempster–Shafer framework. Then, the uncertainty measure of each body of evidence is based on the subjective uncertainty represented as the evidence sufficiency and evidence importance, and the objective uncertainty measure is expressed as the improved belief entropy. Evidence modification of conflict sensor data is based on the proposed uncertainty management approach before evidence fusion with Dempster’s rule of combination. Finally, the fusion result can be applied in real applications. A case study on sensor data fusion for fault diagnosis is presented to show the rationality of the proposed method.