RBUKF算法在GPS实时定位解算中的应用

迭代最小二乘法（iterative least square, ILS）是GPS实时定位解算中使用最为广泛的方法，而近年来扩展卡尔曼滤波（extended Kalman filter, EKF）和无轨迹卡尔曼滤波（unscented Kalman filter, UKF）也在定位解算中逐步得到应用。主要研究了UKF算法的改进型RBUKF算法在GPS实时定位解算中的应用。首先建立了滤波模型，并通过分析和调试得到了滤波器参数，最后使用真实卫星数据对算法进行了验证。实验结果表明：RBUKF算法的定位精度优于EKF和ILS，与UKF基本相同，而其计算量小于UKF和EKF。

Application of RBUKF algorithm in real-time GPS position estimatin

The iterative least square (ILS) algorithm has been widely used in the real-time GPS position. Recently, the extended Kalman filter (EKF) and unscented Kalman filter (UKF) are proposed to use in this problem. The application of the RBUKF, an improved algorithm of UKF, in the real-time GPS position estima-tion is proposed. A filter model is constructed firstly. Then the proper parameters are acquired by analyzing and tuning. The real satellite data are used to test the algorithm finally. 'The result of the experiment shows that the accuracy of RBUKF is better than the EKF and ILS, while equals to the UKF, and its computational complexity is better than the UKF and EKF.