基于ISODATA模糊系统的飞船落点选优方法

针对如何从基于分布式并行计算获得的数千个飞船返回过程预报落点中优选出＂最可能＂落点这一问题,在充分考虑以往返回落点计算的先验知识和最新落点信息的基础上,设计了飞船返回的自适应聚类模糊系统,采用含有时间因子的迭代自组织数据分析算法研究了返回舱落点优选方法。最后,利用＂神舟八号＂返回落点实测数据进行模型检验。计算结果表明,该方法的落点预报精度较传统选优算法的预报精度高50%以上,同时具有较好的稳定性,可为飞船返回搜救提供技术支撑。

Optimizing Spacecraft Impact Points Based on ISODATA Fuzzy System

An adaptive clustering fuzzy system is designed for forecast of the most probable impact point of a space-ship during its return to the earth from thousands of predicted impact points obtained through distributed parallel computing, taking into consideration a priori knowledge in impact point computation and information of the latest impact points. Using IsoDATA （Iterative Self-Organizing Data Analysis Algorithm） with time factor, an impact point optimization model is developed. Test of the model with measured data of Shenzhou VIII spaceship shows that the accuracy of impact point forecast is more than 50% higher than the traditional optimal selection algorithm. Mo-reover, it has higher stability.