星载GPS卫星定轨的UKF-EKF算法

针对无味Kalman滤波（Unscented Kalman Filter）在卫星定轨应用中存在计算效率和估计精度之间如何平衡的问题，本文提出了一种将无味Kalman滤波和扩展Kalman滤波（Extended Kalman Filter）相结合的新算法。该算法对标准的无味Kalman滤波算法作了两个方面的改进，一方面改进采样策略，以最小偏度单形采样策略代替对称采样策略；另一方面改进算法结构，以无味Kalman滤波和扩展Kalman滤波融合算法代替单纯的无味Kalman滤波算法，系统的强非线性部分采用无味Kalman滤波来处理，弱非线性部分采用扩展Kalman滤波来处理。算例结果表明，新算法估计精度与无味Kalman滤波相当，但计算效率提高了30%左右。

Satellite Orbit Determination Algorithm Based on UKF-EKF

According to the problem of how to balance between computation efficiency and estimation accuracy of UKF (Unscented Kalman Filter) in satellite orbit determination applications, this paper puts forward a new algorithm by combining UKF and EKF (Extended Kalman Filter). The algorithm has two aspects improvements compared with the standard UKF algorithm. One is the improvement of the UKF sampling strategy, and with the scaled minimal skew simplex sampling strategy instead of the symmetric sampling strategy; the other one is the improvement of the UKF algorithm structure, and the simple UKF algorithm structure is replaced by the UKF-EKF fusion algorithm structure that the strong nonlinear part of the system is processed by UKF, and the weak nonlinear part of the system is processed by EKF. Numerical results show that the estimation accuracy of the new algorithm is similar with that of UKF, while the computation efficiency is effectively improved.