基于证据理论的模糊信息融合及其在目标识别中的应用

Fuzzy Information Fusion Based on Evidence Theory and Its Application in Target Recognition

信息融合系统中的不确定性信息常常表现为模糊性和随机性信息。提出了一种在证据理论框架下实现模糊信息融合的方法。该方法首先基于随机集理论刻画模糊信息的隶属函数,获得了模糊观测下具有概然特性的似然函数,该似然函数表示在收集的模糊信息下确定为某一目标的可能性,在数值上表示了传感器信息对某一命题支持的程度,利用似然函数确定传感器输出的基本概率指派,最后利用Dempster-Shafer组合规则实现多传感器信息融合。

Evidence theory is widely used in automatic target recognition （ATR） system. One of the problems in real application is that not only the observation collected by sensors, but also the attributes of targets in the model database may be fuzzy too. In this situation, how to automatically determine the mass function of fuzzy information is an open issue. In this paper, a method of automatically determining mass function for target recognition is presented. After representing both the individual attribute of target in the model database and the sensor observation or report as fuzzy membership function, a random sets model of this fuzzy information is introduced. Then, a likelihood function is obtained to deal with the fuzzy data collected by each sensor. The likelihood function has probabilistic character and can be transformed into a mass function, which numerically shows the support degree of the hypotheses that the target is a certain target under the collected fuzzy information. The present approach has been tested in an ATR system to illustrate its efficiency and can be easily used in many fuzzy information fusion systems.