多终端仅测速实时定轨的自适应联邦STCKF算法

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。

Adaptive federated STCKF algorithm for satellite orbit determination of#br# multiple hand-held terminals with only rate data

An adaptive federated strong tracking cubature Kalman filter (STCKF) algorithm is proposed for satellite orbit determination of multiple hand-held terminals with only rate data. First, the improved Euler method is used to disperse the orbital dynamic equation with J2 perturbation. Then, the STCKF algorithm is designed for each terminal, which calculates the suboptimal fading factor based on the equivalent representation of strong tracking filter (STF) to adjust the gain matrix online. Then, local orbit determination result of each terminal is fused by the optimal fusion algorithm. In order to improve the accuracy of fusion, the information distribution factor is determined adaptively by Frobenius norm of error covariance matrix. Finally, the simulation results show that improved Euler method can achieve a higher discrete precision of orbital dynamics equation, and the adaptive federated STCKF algorithm can improve the accuracy and convergence speed of real-time orbit determination.