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题名: Probabilistic Data Association Method for Space Object Tracking
作者: Xu Zhanwei; Wang Xin
刊名: Acta Astronomica Sinica
出版日期: 2017
卷号: 58, 期号:3, 页码:26-1-26-8
英文摘要: 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.
语种: 英语
内容类型: 期刊论文
URI标识: http://libir.pmo.ac.cn/handle/332002/17364
Appears in Collections:空间目标与碎片观测研究基地_期刊论文

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