卡尔曼滤波的中长基线非差RTK算法

Un-differenced RTK algorithm for medium and long baseline based on Kalman filter

针对实时动态定位(RTK)中作业范围受到大气延迟误差制约的现象,该文提出了一种基于卡尔曼滤波的非差观测模型RTK算法和非差改正数的计算方法。利用扩展卡尔曼滤波函数模型,将残余的相对对流层延迟、相对电离层延迟同流动站位置参数以及单差整周模糊度作为状态向量进行卡尔曼滤波估计。非差观测模型利用参考站的非差误差改正数以单颗卫星为对象进行误差改正,流动站接收数据小,算法简单。通过GNSS实测数据对该算法进行了算法验证和结果分析,实验结果表明,对于中长基线,利用非差观测模型可实现GNSS单参考站RTK定位,并获得厘米级的定位精度。

Aiming at the phenomenon that the operating range of the real-time kinematic(RTK) is restricted by atmospheric delay,in this paper,a un-difference observation model RTK algorithm based on Kalman filter and a calculation method of un-difference correction number was proposed. Using the extended Kalman filter function model,the residual relative tropospheric delay,relative ionospheric delay and rover position parameters,and single-difference ambiguity were used as state vectors for Kalman filter estimation. The un-difference observation model used the un-difference error correction number of the reference station to make error correction for a single satellite. The receiving data of the rover was small and the algorithm was simple. The algorithm was verified by the GNSS measured data and the results were analyzed. The experimental results showed that for the medium and long baseline,the un-difference observation model could realize the GNSS single reference station RTK positioning and obtain the centimeter-level positioning accuracy.