脉冲星方位误差估计的TSKF算法

为提高脉冲星方位误差估计对方位自行速度及卫星位置误差的鲁棒性和整体运算的高效性，设计了两级卡尔曼滤波（TSKF）算法。首先，分析了方位自行速度及卫星位置误差对方位误差估计的影响，并分别结合相关算法进行了仿真验证。然后，结合方位误差估计的CV模型和两级卡尔量滤波的相关原理，写出了TSKF算法的更新方程，并分析了实现并行计算的基本流程。仿真实验的数据显示:在方位自行速度及卫星位置误差均存在的情况下，TSKF算法的方位估计精度约为0.1 mas，方位自行速度估计精度约为1.1 mas/a；与基于CV模型的估计算法相比，TSKF算法的浮点运算仅增加了0.048%。 

TSKF algorithm for pulsar position error estimation

In order to improve the robustness of the pulsar position error estimation to the proper motion and satellite position error and the efficiency of the overall algorithm, a two-stage Kalman filter (TSKF) algorithm is designed. Firstly, the influences of pulsar proper motion and satellite position error on pulsar error estimation are analyzed, and the simulation results are verified by combining relevant algorithms. Secondly, based on the CV model and the principle of two-stage Kalman filter, the update equations of TSKF algorithm are derived, and the basic flow of parallel computing is analyzed. The data of the simulation experiment show that position accuracy of the TSKF algorithm is about 0.1 mas and corresponding proper motion accuracy is about 1.1 mas/a in the case of both proper motion and satellite position error. Compared with the estimation algorithm based on CV model, the floating point operation of TSKF algorithm only increases by 0.048%.