基于集合距离的信息优势度量方法

当前信息优势度量研究混淆了信息优势及其效能等概念,度量指标缺乏严格数学基础,存在操作性不强、解释力不足、适用范围有限等缺点。针对上述问题,提出了一种基于集合距离的信息优势度量方法,通过引入感知信息集合与客观信息集合的距离度量,重新定义信息质量、信息优势等重要概念。详细讨论了最优子模式分配等几种常用集合距离的选取,并利用基于Agent的同质化协同模型对最优子模式分配距离下的信息质量进行了初步验证。结果表明,该方法具备可操作性,可更好反映信息优势局部和动态特性,且在部分条件下可退化为传统方法,灵活性和兼容性更强。

Measurement of information superiority based on set distance

The concepts of information superiority and its efficiency are always confused in current research and there are few measurement metrics based on rigorous mathematical foundation. That causes the problems such as lack of operability, insufficient in explanation, limited scope of application and so on. To solve these problems, a new method based on set distance is presented, and the information quality, information superiority and other important concepts are redefined by calculating set distance between situation awareness information collection and objective information collection. The selection of the commonly used set distances, such as the optimal subpattern assignment (OSPA) distance, is discussed in detail, and by the Agent model of homogeneous coordination, the information quality defined by OSPA distance is preliminarily simulated and verified. The results show that the new method is operable, and can better reflect the local and dynamic characteristics of information superiority. Besides, the new method is more flexible and applicable, and under some conditions it degenerates into traditional ones.