航天侦察装备体系指标关联信息挖掘研究

Research on Assocoation Information Mining of Space Reconnaissance Equipment System Index

航天侦察装备体系的体系效能和体系贡献率影响因素指标数量多,指标间关系复杂。如何识别关联,优选关键性能指标并明确性能指标与体系贡献率之间的关系是体系效能及贡献率评估问题的关键。通过MATLAB与STK联合仿真获取航天侦察装备底层指标数据,基于FP-Tree(Frequent Pattern-Tree)算法发现指标间关联信息,去除冗余,确定指标关联类型,并结合Marichal熵建立优化模型确定关键性能指标的贡献度。仿真实验结果表明,通过FP-Tree算法可挖掘航天侦察装备初始评价指标体系间的关联性及关联类型,确定指标贡献度并实现指标体系的精简。

The system effectiveness and system contribution rate of the Space Reconnaissance Equipment System(SRES) has a large number of mutally associated indicators. How to identify relationships the association, select the key indicators and clarify the assocition between core indicators and system contribution rate are the key of the evaluation of system effectiveness and contribution rate. Through the joint simulation of MATLAB and STK, the underlying index data of SRES is obtained. Based on the Frequent Pattern-Tree(FP-Tree) algorithm, the assocition information is discovered, the redundancy is removed and the type of indicator assocition is determined, and an optimization model is established to determine the contribution of the key indicators by combining Marichal entropy. Simulation experiment results show that the FP-Tree algorithm can be used to mine the assocition and its types between the initial evaluation index system, determine the index contribution and realize the streamlining of the index system.