卫星跟踪中位置预测的序列匹配算法

Sequential matching algorithm of position prediction for satellite tracking prediction for satellite tracking

针对卫星跟踪中的位置预测问题,分析了动力学模型方法的预测误差组成,提出一种用测量数据来计算预测数据时间误差的序列匹配算法,并讨论了空间误差的计算方法。仿真结果表明,提出的时间校正和空间误差补偿方法的预测精度比动力学模型和Kalman滤波法的预测精度高;即在相同的精度要求下,序列匹配算法能预测的时间范围是Kalman滤波法的1.5倍以上。

Focused on position prediction of satellite tracking, the constitution of prediction errors of dynamics model algorithm is analyzed. A sequential matching algorithm with measurement data to predict temporal errors of prediction data is presented and a method to calculate spatial errors of prediction is discussed. Simulation results show that the prediction accuracy of the algorithm for temporal correction and spatial compensation presented in the paper is higher than that of dynamic model or Kalman filtering algorithm. For same accuracy requirement, prediction time of the sequential matching algorithm is 1.5 times longer than that of Kalman filtering algorithm.