带摄动力拟合的低轨卫星实时定轨STCKF算法

发布时间：2018-04-28 20:33

本文选题：摄动力 + 多项式拟合　；参考：《电波科学学报》2016年05期

【摘要】：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.
[Abstract]:A strong tracking volume Calman filter (Strong Tracking Cubature Kalman Filter, STCKF) is proposed in order to reduce the precision of orbit determination in the real-time orbit determination of low orbit satellite, which leads to the imprecision of the initial value of the filter and the imprecision of the orbit model. The equivalent representation of the calculation times through the strong tracking filter (Strong Tracking Filter, STF) is given. The optimal fading factor adjusts the gain matrix on line in real time, forcing the residual sequence to be orthogonal to each other, effectively reducing the sensitivity to the initial state. The Euler prediction correction method is used to discretize the orbital dynamics equation with the J_2 perturbation, and the other perturbation forces are expressed by the polynomial fitting function to improve the model accuracy. The STCKF algorithm of dynamic fitting can effectively improve the accuracy of real-time orbit determination and reduce the dependence of orbit determination accuracy on the initial filtering value.

【作者单位】：装备学院研究生院;装备学院光电装备系;西安卫星测控中心;
【基金】：国家高技术研究发展计划(2015AA7026085)
【分类号】：V448.2

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