基于时间误差修正与多特征联合判决的空间碎片测轨数据关联方法
A Track-Catalogue Correlation Method for Space Debris Utilizing Time Error Correction and Multi-Features Joint Judgements
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摘要: 针对大批量空间碎片的编目管理, 广域监视测轨数据与编目目标的关联匹配是前提和关键, 关联正确率不仅影响正常编目处理, 也影响观测数据利用率和监视体系效能. 提出一种改善大批量空间碎片测轨数据关联正确率的方法. 首先, 根据轨道预报误差特点, 建立了将大尺度轨道空间位置误差向小尺度时间误差转换的时间误差参数估计与观测量残差修正模型; 然后, 构造基于时间误差参数(包括常数项和线性项)、修正后观测量残差RMSE统计值以及关联价值函数等四特征量联合的关联判决模型, 并给出关联判决门限设置策略和关联处理流程; 最后, 进行了光电望远镜(阵)的仿真和实测数据验证, 结果显示该方法的测轨数据-目标关联正确率可达约98%的水平.Abstract: Track-Catalogue correlation is the precondition and foundation of large scale space object cataloging maintenance. The accuracy of correlation not only affects normal cataloging processing, but also affects the utilization of observation data and the effectiveness of space object surveillance system. In this paper, a method is put forward to improve the correlation accuracy of large-batch orbital track data. Firstly, based on the characteristics of orbit error propagation, a model is constructed to estimate the orbital prediction time error and to correct observation residual, aiming to transfer the large scale spatial error to a small scale time-domain error. Secondly, a correlation judgement model involving a four-parameter-joint feature vector is proposed, with threshold setting guidelines and a data correlation processing flow followed. Finally, some examples with regard to large-batch simulated and actual measured tracks are checked to illustrate the effectiveness of the method.