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针对多手持终端仅测速实时定轨问题,设计了一种自适应联邦强跟踪容积卡尔曼滤波(strong tracking cubature Kalman filter,STCKF)算法。首先,使用欧拉预测校正法对带J2项摄动的轨道动力学方程进行离散得到状态方程。然后,为每个手持终端设计了STCKF滤波算法,该算法基于强跟踪滤波(strong tracking filter,STF)的等价表示计算次优渐消因子以在线实时调整增益矩阵,提高短弧段内滤波估计收敛速度。进而,利用信息最优合成算法对每个终端输出的局部定轨结果进行融合,为提高信息融合精度,信息分配因子由误差协方差矩阵的Frobenius范数自适应确定。最后的仿真结果表明,欧拉预测校正法可以有效提高轨道动力学方程离散精度,自适应联邦STCKF算法可以有效提高实时定轨精度和滤波收敛速度。
In order to solve the problem of real-time orbit determination in multi-handheld terminals, an adaptive federal strong tracking cubature Kalman filter (STCKF) algorithm is designed. First, Euler’s predictive correction method is used to discretize the orbital dynamics equation with the perturbation of J2 to obtain the state equation. Then, an STCKF filtering algorithm is designed for each hand-held terminal. The algorithm calculates the suboptimal fade-out factor based on the equivalent representation of strong tracking filter (STF) to adjust the gain matrix in real time and improve the filtering estimation convergence speed. Furthermore, the local optimal orbit determination results output by each terminal are fused by the information optimal composition algorithm. In order to improve the information fusion accuracy, the information distribution factor is adaptively determined by the Frobenius norm of the error covariance matrix. The final simulation results show that the Euler prediction and correction method can effectively improve the discrete accuracy of the orbital dynamics equation. The adaptive federal STCKF algorithm can effectively improve the real-time orbit determination accuracy and the convergence rate of the filter.