由于卫星导航定位方程组的非线性特性，传统的定位解算方法在解算之前需要对定位方程组进行线性化处理，会影响定位解算精度。容积卡尔曼滤波算法基于贝叶斯估计和容积变换，可以有效地提高非线性滤波估计的精度。将容积卡尔曼滤波算法应用于卫星导航定位解算，建立定位解算系统状态转移模型和测量模型，通过时间更新和测量更新两个环节递推估计接收机的三维位置坐标。利用IGS观测数据对基于容积卡尔曼滤波的定位解算方法进行验证，并与最小二乘法和扩展卡尔曼滤波算法进行对比，结果证明将容积卡尔曼滤波用于卫星导航定位解算可以获得更高的定位精度，并且具有较快的收敛速度。
Due to the nonlinear characteristics of satellite navigation positioning equations, the traditional positioning methods need to linearize the equations before solving, which would reduce the accuracy of positioning. Cubature Kalman filter (CKF) algorithm based on Bayesian estimation and cubature transformation could effectively improve the accuracy of nonlinear filtering estimation. In this paper, cubature Kalman filter algorithm is applied to satellite positioning solution. The transfer and measurement models of the positioning solution system are established. The three-dimensional position coordinates of the receiver are recursively solved by time update and measurement update. The positioning solution algorithm based on cubature Kalman filter is verified by IGS observation data tests, and compared with the least square method and extended Kalman filter algorithm. The results show that the application of CKF algorithm in satellite positioning is of higher precision and faster convergence speed.
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