近年来，许多领域都在进行多传感器数据融合技术的研究。多传感器数据的属性融合有很多算法，最常用的算法是贝叶斯决策检验法，国际上已提出将证据理论用于数据融合，但在这方面的理论基础还不完善。本文研究了证据理论在多传感器数据融合中的应用。Ｄｅｍｐｓｔｅｒ－Ｓｈａｆｅｒ方法是对Ｂａｙｅｓ决策检验法的推广，证据理论比概率论满足更弱的公理系统，并且在区分不确定与不知及精确反映证据收集过程等方面显示了很大的灵活性。文中阐述了Ｄ－Ｓ证据理论的数学性质，给出了可信度公理及Ｄ－Ｓ综合规则，并进行了计算机仿真实验，实验结果说明这种判决方法非常实用，用于数据融合算法非常有效 In recent years, many areas of multi-sensor data fusion technology are under study. Multi-sensor data fusion has many algorithms. The most commonly used algorithm is Bayesian decision-making test method. Evidence theory has been proposed for data fusion in the world, but the theoretical foundation in this area is still not perfect. This paper studies the application of evidence theory in multisensor data fusion. The Dempster-Shafer method is a generalization of the Bayesian decision-making test. Evidence theory satisfies a weaker axiom than probabilistic theory and shows great flexibility in distinguishing between uncertainty and ignorance, as well as in accurately reflecting the evidence collection process. The paper expounds the mathematical properties of D-S evidence theory, gives the axioms of credibility and D-S synthesis rules, and conducts computer simulation experiments. The experimental results show that this method is very useful and the data fusion algorithm is very effective