分布式星敏感器下空间目标航迹段关联算法

针对空间目标可以断续地被分布式星敏感器观测到的特性,将不同时段空间目标在星敏感器下的观测信息关联作为基于星敏感器进行空间目标精确定轨的前提。结合空间目标运动特性,在以往双门限模糊关联的基础上,加入轨道平面法向量约束,筛选候选关联对象,简化关联运算成本,提出了分布式星敏感器下空间目标航迹段关联算法。通过仿真分析了6组噪声级别下,航迹外推误差随时间的发散情况,并且给出模糊隶属度函数中的调整系数的参考值,使长间隔下不同目标的关联相似度区别更显著。仿真表明在一定噪声下所提算法关联准确率高于最邻近关联和传统模糊关联,初定轨误差在各轴位置的标准差为6 km,各轴速度标准差为6 m/s时,可区分最小相位差为0.5°的相邻目标,间隔2 h关联准确率达98%,间隔7 h时关联准确率达90%。

Algorithm of space track segment association under distributed star sensor

In view of the characteristic that the space targets can be observed intermittently by distributed star sensors, the observation information association of the space target in different time periods under star sensors is taken as the premise of accurate space object calibration based on the star sensors. The space target motion characteristic is considered. Combined with the characteristics of the space traget motion, on the basis of the previous two threshold fuzzy correlation, the track segment association algorithm of the space target under the distributed star sensor is proposed by adding the normal vector constraint of orbit plane, filtering the candidate association objects and simplifying the cost of association operation. Through the simulation, the divergence of the track extrapolation error with time is analyzed under six sets of noise levels. And the reference value of the adjustment coefficient in the fuzzy membership function is given, which makes the correlation similarity of different targets more significant under long interval. The simulation results show that the correlation accuracy of the proposed algorithm is higher than that of the nearest neighbor and the traditional fuzzy association under certain noise. When the standard deviation of the initial orbital error is 6 km at each axis position and 6 m/s at each axis velocity, the adjacent targets with a minimum phase difference of 0.5° can be distinguished. Correlation accuracy of interval 2 h is 98%, and the correlation accuracy is 90% of 7 h interval.