空间目标跟踪的概率数据关联方法  

Probabilistic Data Association Method for Space Object Tracking

作　　者：许占伟[1,2] 王歆[1,2] 

机构地区：[1]中国科学院紫金山天文台,南京210008 [2]中国科学院空间目标与碎片观测重点实验室,南京210008

出　　处：《天文学报》2017年第3期89-96,共8页Acta Astronomica Sinica

基　　金：中国科学院国防科技创新基金项目(CXJJ-15S129)资助

摘　　要：在空间目标的光学观测中,由于跟踪波门内多个量测事件频发,导致跟踪量测的不确定,降低自动跟踪精度引起跟踪不稳.结合Kalman滤波和概率数据关联算法,实现了空间目标自适应跟踪.方法通过Kalman滤波预测确定关联区域,采用概率数据关联技术获得等效量测作为Kalman滤波的有效馈源.实验表明:方法可以有效地提高自动跟踪精度,改善空间目标自动跟踪鲁棒性.In the optical tracking of space objects, multiple measurements are often detected in the observing gate, which brings about the uncertainty in the tracking accuracy and causes the unstability along the tracking path. This kind of condition will eventually interrupt the track and lead to the lost of the target. A new approach, combining the Kalman filter and probabilistic data association, is proposed for the adaptive tracking of space objects. This method employs Kalman filter to predict the gate of association, and uses probabilistic data association to obtain the equivalent measurement as an effective feed instead. The experiments show that this technique can effectively improve the tracking accuracy as well as the robustness for the automatic tracking of space objects.

关 键 词：天体测量学 航天器 望远镜 方法 统计 

分 类 号：P129[天文地球—天体测量]