Multi-sensor data fusion based on the belief divergence measure of evidences and the belief entropy
作者: Fuyuan Xiao
作者单位: 1School of Computer and Information Science, Southwest University, No.2 Tiansheng Road, BeiBei District, Chongqing 400715, China
刊名: Information Fusion, 2019, Vol.46 , pp.23-32
来源数据库: Elsevier Journal
DOI: 10.1016/j.inffus.2018.04.003
关键词: Sensor data fusionDempster–Shafer evidence theoryEvidential conflictBelief divergence measureJensen–Shannon divergenceBelief entropyFault diagnosis
原始语种摘要: Abstract(#br)Multi-sensor data fusion technology plays an important role in real applications. Because of the flexibility and effectiveness in modeling and processing the uncertain information regardless of prior probabilities, Dempster–Shafer evidence theory is widely applied in a variety of fields of information fusion. However, counter-intuitive results may come out when fusing the highly conflicting evidences. In order to deal with this problem, a novel method for multi-sensor data fusion based on a new belief divergence measure of evidences and the belief entropy was proposed. First, a new Belief Jensen–Shannon divergence is devised to measure the discrepancy and conflict degree between the evidences; then, the credibility degree can be obtained to represent the reliability of the...
全文获取路径: Elsevier  (合作)
影响因子:2.262 (2012)

  • entropy 平均信息量
  • divergence 分歧
  • conflict 冲突
  • credibility 可信度
  • measure 测度
  • fusion 融解
  • information 报告
  • weights 法码
  • proposed 建议的
  • flexibility 柔顺性