跟踪站分布对非组合精密单点定位提取GPS卫星差分码偏差的影响

Effect of Tracking Stations Distribution on the Estimation of Differential Code Biases by GPS Satellites Based on Uncombined Precise Point Positioning

  • 摘要: 提出利用非组合精密单点定位获取跟踪站和卫星差分码偏差(differential code bias,DCB)的电离层观测量,并结合“IGGDCB(institute of geodesy and geophysics DCB)两步法”精确分离电离层斜延迟与DCB参数的新思路。为了研究跟踪站的分布对上述方法提取卫星DCB的影响,本文分别选取欧洲区域集中分布和全球均匀分布的不同数量IGS(international GNSS service)跟踪站,利用太阳活动高峰期间连续15 d的实测数据进行卫星DCB的提取实验,并将结果与CODE(center for orbit determination in Europe)发布的DCB当月产品进行比较。实验结果表明,本文提出的方法可以精确提取卫星DCB,其精度优于载波相位平滑码方法,其中,采用欧洲区域的跟踪站提取差异的RMS优于0.2 ns,而全球分布的跟踪站提取差异的RMS优于0.1 ns,全球布站有利于同时提高RMS和单天解稳定性,并且随着跟踪站数量的增加,卫星DCB单天解的稳定性将会得到提高。

     

    Abstract: Ionospheric observations including the slant total electron content (STEC) and satellite-receiver's differential code bias(DCB) can be extracted precisely by an uncombined precise point positioning(PPP) technique. Moreover, a GPS satellite DCB can be determined using the “IGGDCB” approach at high accuracy. A new strategy for combining uncombined PPP and “IGGDCB” is proposed in this paper to investigate the effect of distribution of tracking stations on the estimation of DCB in GPS satellites. Consecutive 15-day data from different numbers of IGS stations in Europe and globally during a high solar activity period were chosen for this study. In order to assess the precision of satellite DCB estimates in this experiment, CODE monthly satellite DCB estimates were considered as the true value. Our experimental results show that satellite DCB can be calibrated precisely by the proposed method. The RMS of the difference between DCB estimates and corresponding CODE value is less than 0.2 ns based on the data from Europe, while it is less than 0.1ns based on the global data. These results were better than the results from the smoothed code data. In addition, the day-to-day variation can be reduced with the increase of contributed stations, whilst the RMS and day-to-day variation of daily satellite DCB estimates can be both reduced based on the global data.

     

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