A weighted belief entropy-based uncertainty measure for multi-sensor data fusion

Yongchuan Tang, Deyun Zhou, Shuai Xu, Zichang He

Research output: Contribution to journalArticlepeer-review

87 Scopus citations

Abstract

In real applications, how to measure the uncertain degree of sensor reports before applying sensor data fusion is a big challenge. In this paper, in the frame of Dempster–Shafer evidence theory, a weighted belief entropy based on Deng entropy is proposed to quantify the uncertainty of uncertain information. The weight of the proposed belief entropy is based on the relative scale of a proposition with regard to the frame of discernment (FOD). Compared with some other uncertainty measures in Dempster–Shafer framework, the new measure focuses on the uncertain information represented by not only the mass function, but also the scale of the FOD, which means less information loss in information processing. After that, a new multi-sensor data fusion approach based on the weighted belief entropy is proposed. The rationality and superiority of the new multi-sensor data fusion method is verified according to an experiment on artificial data and an application on fault diagnosis of a motor rotor.

Original languageEnglish
Article number928
JournalSensors
Volume17
Issue number4
DOIs
StatePublished - 22 Apr 2017

Keywords

  • Dempster–Shafer evidence theory
  • Deng entropy
  • Sensor data fusion
  • Uncertainty measure
  • Weighted belief entropy

Fingerprint

Dive into the research topics of 'A weighted belief entropy-based uncertainty measure for multi-sensor data fusion'. Together they form a unique fingerprint.

Cite this