基准站观测数据加密方法及其在差分GNSS后处理中的应用

A Densification Method for Base Station Observation Data and its Application to Post Processing of Differential GNSS

  • 摘要: 当基准站采样率低于流动站时,不能用常规差分全球卫星导航系统(Global Navigation Satellite System,GNSS)后处理方法得到流动站所有历元坐标。针对此问题,给出了基于精密单点定位(precise point positioning,PPP)模型构造基准站非采样点上虚拟观测值的方法。该方法将接收机钟差、对流层天顶湿延迟从观测值误差中分离出来,同消电离层模糊度一起进行估计,利用基准站真实坐标获得卫地距,在此基础上计算相邻两个观测历元的残差进而拟合历元间非采样点残差,与卫地距、各项估计误差一起生成虚拟观测值。该方法保持了虚拟观测值的误差特性,尤其是基准站与流动站间的共性误差。该方法仅加密基准站数据,对流动站没有影响。算例结果表明,基准站在采样间隔30 s范围内,使用该方法加密的虚拟观测值与真实值有较好的一致性,采样间隔分别为30 s、15 s、5 s的虚拟测码伪距和载波相位观测值偏差的标准中误差分别在0.2 m和1.2周左右、0.1 m和0.7周左右、0.05 m和0.2周左右;在30 s采样间隔情况下,按照该方法处理后仍能满足厘米级定位精度的要求。

     

    Abstract: When the sampling rate of base station is lower than that of the rover station, it's impossible to obtain the coordinates of the rover station at all epochs with a conventional differential GNSS post-processing method. To solve this problem, a method based on PPP model is proposed to construct the virtual observation data of the non-sampling points of base station. The method separates the receiver clock error and tropospheric zenith wet delay from the observation error, and estimates them simultaneously with ionosphere ambiguity, after which the distance between the satellite and the station is obtained by base station's real coordinate. On this basis, the method calculates the residual errors of two adjacent epochs, by which the residual errors of non-sampling epochs are fitted. Eventually, the virtual observation data are generated by the non-sampling epochs' residual errors, the distance between the satellite and the station and the estimated errors. Error characteristic of the virtual observation data is maintained, especially the common error between the base station and the rover station; This method only densifies the data of the base station, which will not affect the rover station. The experiment results show that when base station works with a sampling interval within 30 s, the virtual observation data generated by this method are accordant with the real data. In 30s, 15s and 5s sampling intervals, the standard errors of virtual observations of pseudo-range are about 0.2, 0.1 and 0.05 meters, and virtual observations of carrier phase are about 1.2, 0.7 and 0.2 cycles, respectively; In the case of 30 s sampling interval, the positioning result obtained by this method can still meet the cm level accuracy requirements.

     

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