基于证据理论和混淆矩阵的传感器可靠性评估

在信息融合系统中，各传感器提供的信息不一定完全可靠，在融合前有必要对传感器的可靠性进行评估，进而对其提供的信息进行预处理。基于证据理论，在传感器混淆矩阵的基础上定义了后验概率向量，通过分析后验概率向量与传感器输出证据之间的关系对传感器可靠性进行评估；然后利用传感器的可靠性因子对证据进行折扣运算，实现对信息的预处理；最后利用Dempster组合规则进行融合。基于证据理论的融合识别算例表明，所提出的方法综合利用了传感器的先验信息和动态输出，可以较好地反映传感器的性能，并能够有效降低可靠性传感器的影响，具有较好的融合效果。

Evaluating dynamic reliability of sensors based on evidence theory and confusion matrix

In the information fusion system, information provided by sensors is always not fully reliable. Thus it is necessary to evaluate the reliability of sensors for processing information conveyed by them. Therefore, the posterior probability is defined based on the confusion matrix of sensors. The reliability of sensors is evaluted by analyzing the relation between the acquired evidence and posterior probability. The evidence provided by sensors is discounted by using reliability factors of sensors. The discounted evidences are combined by using the Dempster’s combination rule. Numerical simulation demonstrates that the performance of sensors can be evaluated better and the fusion result can be improved by reducing the influence of low-reliability sensors.