摘要
提出了一种基于模糊集合和证据理论的信息融合方法.针对证据理论应用中基本概率指派函数(mass函数)以及多传感器信息融合中各个传感器测量数据的可信度均难以确定的问题,首先利用传感器测量信息的不确定性得到辨识框架的隶属度函数;然后利用隶属度函数构造证据理论的mass函数;再根据各mass函数之间的距离评估各传感器的相对可信度,在此基础上对各个证据进行折扣,利用基于折扣系数法的改进证据理论组合规则对多传感器信息进行融合.最后将所提出的方法应用于目标识别系统中.仿真结果表明,在证据高度冲突时,该方法仍能正确识别目标,提高了信息融合系统的稳定性.
In this paper,an information fusion method based on fuzzy set and evidence theory was proposed.Firstly,the fuzzy membership function of the recognition framework was obtained from the uncertain observation collected by the sensor.Then by using the membership function,the mass function could be obtained.After that,the evidence distance was used to determine the credibility and the discounting factor of each sensor.The present approach was tested in a target recognition system to illustrate its efficiency.The simulation results show that even if the evidences are conflict with each other in a high degree,correct results can be obtained.
出处
《哈尔滨工业大学学报》
EI
CAS
CSCD
北大核心
2012年第3期107-111,共5页
Journal of Harbin Institute of Technology
基金
航空科学基金资助项目(20090112002)
关键词
信息融合
模糊证据理论
折扣系数法
冲突
目标识别
information fusion
fuzzy evidence theory
discounting
conflict
target recognition
