从D-S证据理论的基本概念和识别框架出发, 提出一种新的符合探测器特征条件的基本概率赋值函数的构造算法, 将探测器的探测信号对不同目标身份的模糊隶属度通过处理得到的识别判定精度作为探测器的基本概率赋值函数值, 进行D-S证据融合。还给出了一个三种探测器目标识别的实例, 结果表明, 该方法得到的融合结果提高了目标识别的可信度,减小识别的不确定性。

From the basic conception and the recognition frame of Dempster-Shafer (D-S) evidential theory, a new construction algorithm to basic probability assignment function based on sensor characteristics is proposed. The fuzzy membership for different target identity of the sensing signals from the sensors is processed to obtain the judging precision, which is used as the basic probability assignment function value to perform D-S evidence fusion. An example for the simulations of target recognition focused on three types of sensors is presented, the results show that the method is good to improve the reliability of the target recognition and decreases the uncertainty.