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J_2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. Aiming at the problem of low accuracy of orbit determination due to inaccurate filtering orbit model in real-time orbit determination of low-orbit satellites, a Strong Tracking Cubature Kalman Filter (STCKF) algorithm with perturbation fitting is proposed . By calculating the suboptimal fade-out factor through the equivalent representation of Strong Tracking Filter (STF), the gain matrices are adjusted online in real time to force the residual sequences to be orthogonal to each other, effectively reducing the sensitivity to the initial state.Using Euler The predictive correction method is used to discretize the orbital dynamics equation with J_2 perturbation and the rest perturbation force is expressed by the polynomial fitting function to improve the model accuracy.The simulation results show that STCKF with perturbation fitting can effectively improve the real-time orbit determination accuracy , And reduces the dependence of the orbit determination accuracy on the initial filter.