基于广义相对信息熵的数据融合系统性能评估

提出基于度量融合系统信息量增加量的性能评估方法。该方法利用广义相对信息熵作为度量数据融合使系统信息量增加量的指标。将各种常见的不确定信息在模糊测度的框架中表达，利用Shapley熵测度系统信息的信息量。将相对熵推广到模糊测度的Shapley熵中，其归一化值作为定量评价数据融合系统性能的标准，该方法在模糊测度的框架中度量融合系统对系统信息质量的提高，普适性强，是数据融合系统性能评估的一种有效方法。

Evaluation for Data Fusion System Based on Generalized Relative Entropy

Evaluation for data fusion system is discussed based on relative entropy. The method measures relative information gain with generalized relative entropy. Some of the most common types of uncertain information can be represented in the framework of fuzzy measure. Shapley entropy is introduced to the framework to calculate the amount of information in a fuzzy measure. The relative entropy to fuzzy measures is generalized. Normalized relative entropy is utilized to evaluate data fusion system. This method measures the information gain of data fusion system in the framework of fuzzy measures. Its robustness is good. It is a effective method to evaluate data fusion system.