附加基线矢量约束的双站协同精密定位方法

传统精密单点定位(PPP)具有高精度、操作方便等诸多优点,其通常利用Kalman滤波进行未知参数的解算,但是定位性能依赖于准确的动态模型和滤波初值,如果动态模型不准确或者滤波初值设定的不正确会导致滤波性能下降甚至发散.针对该问题,提出了一种附加先验的基线约束信息的双站协同PPP定位方法,算法利用双站所成基线的方向信息和长度信息对Kalman滤波过程中双站位置的估计值进行修正,减小了浮点解的误差协方差矩阵,提高了浮点解的精度.利用实测的全球定位系统(GPS)数据进行PPP实验,实际结果表明,与传统PPP参数估计模型相比,本方法有效改善了定位的精度,缩短了收敛时间.

Two-station cooperative precision positioning algorithm with additional baseline constraints

Traditional Precise Point Positioning (PPP) has many advantages such as high accuracy and easy operation. PPP usually uses the Kalman filtering (KF) to solve unknown parameters. However, the positioning performance depends on the accurate kinematic model and filtering initial value. The inaccurate kinematic model or initial filtering value will lead to filter performance degradation or even divergence. In order to solve this problem, this paper proposes a two station cooperative PPP positioning method with additional baseline constraint information. The algorithm uses the direction information and length information of the baseline to modify the estimated position of the two stations. By reduces the error covariance matrix of the floating-point solution, the algorithm improves the accuracy of the floating-point solution. Further validation test based on real GPS data shows that results from baseline vector constraint PPP effectively be improved compared with the traditional PPP parameter estimation method.