带摄动力拟合的低轨卫星实时定轨STCKF算法

针对低轨卫星实时定轨过程中滤波初值及轨道模型不精确导致定轨精度降低的问题, 提出一种带摄动力拟合的强跟踪容积卡尔曼滤波(Strong Tracking Cubature Kalman Filter, STCKF)算法.通过强跟踪滤波(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, 强迫残差序列相互正交, 有效降低了对初始状态的敏感性.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, 用多项式拟合函数表示其余摄动力以提高模型精度.仿真结果表明, 带摄动力拟合的STCKF算法可以有效提高实时定轨精度, 并且降低了定轨精度对滤波初值的依赖.

An STCKF algorithm for LEO satellite orbit determination with disturbing fitting function

A strong tracking cubature Kalman filter(STCKF)algorithm with disturbing fitting func-tion is proposed for Leo satellite orbit determination when inaccurate initial value and orbit model lead to low precision of filter.The suboptimal fading factor is calculated using the equivalent expression of strong tracking filter(STF)to adjust the gain matrix online and to force the residual sequence orthogonal to each other,which effectively reduces the sensitivity to the initial state.Improved Eular method is used to dis-perse the orbital dynamic equation with J 2 perturbation,and the polynomial fitting function is used to re-present the rest disturbing force.The simulation results show that STCKF with disturbing fitting function can effectively improve the orbit determination accuracy,and reduce the dependence on initial value of the filter.