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由于卫星轨道观测数据中含有非线性影响因素,必然会降低定轨精度。将半参数回归模型引入卫星批处理定轨方法中,基于半参数回归模型补偿最小二乘估计法,提出了一种卫星事后轨道改进方法,以降低非线性影响并提高定轨精度。当测量数据存在非线性影响因素时,在理论上证明了半参数回归模型补偿最小二乘估计法优于经典最小二乘估计法。最后,对中低轨卫星定轨进行了仿真,结果表明基于半参数回归模型补偿最小二乘估计法的卫星事后轨道改进方法分离出观测数据中的非线性影响因素,从而提高定轨精度。
As satellite orbit observation data contains nonlinear factors, it will inevitably reduce the orbit determination accuracy. The semi-parametric regression model is introduced into the satellite orbit determination orbit determination method. Based on the semi-parametric regression model to compensate the least-squares estimation method, a satellite after-orbit improvement method is proposed to reduce the nonlinear influence and improve the orbit determination accuracy. When nonlinear factors exist in the measured data, it is theoretically proved that the least squares estimation method is better than the classical least square method in the semi-parametric regression model. Finally, the orbit determination of LEO satellites is carried out. The results show that the nonlinear orbit factors in satellite observational data can be separated by using satellite least squares method based on semi-parametric regression model to improve orbit determination accuracy.