中长基线单基准站动态定位卡尔曼滤波算法

为了削弱站间星间双差对流层延迟和电离层延迟的影响,实现中长基线实时动态高精度定位,对流层干延迟用Hopfield模型计算,站间星间双差对流层湿延迟用相对对流层天顶延迟估计;站间星间双差电离层延迟用相对电离层天顶延迟估计;在此基础上,将流动站位置参数、相对对流层天顶延迟、相对电离层天顶延迟以及站间星间双差整周模糊度作为状态向量进行卡尔曼滤波估计.经验证,该算法初始历元数少,单历元定位平面中误差小于2 cm,高程中误差小于5 cm. 

Kalman Filter Algorithm for Medium-Range Real-Time Kinematic Positioning with One Reference Station

In order to achieve real-time kinematic (RTK) accurate positioning for medium-range baselines and weaken the effects of double-difference tropospheric delay and ionospheric delay between stations and satellites, dry tropospheric delay was computed with the Hopfield model, while double-difference tropospheric wet delay was estimated with a relative tropospheric zenith delay (RTZD) parameter. The double-difference ionospheric delay was estimated with relative ionospheric zenith delay (RIZD) between two stations. A Kalman filter algorithm was proposed by taking position parameters, relative tropospheric zenith delay, relative ionospheric zenith delay and double-difference ambiguities as state variables to be estimated. The experimental results show that the proposed algorithm only needs a few of epochs to initialize, and the mean square error of one epoch is less than 2 and 5 cm in the horizontal and vertical directions, respectively.