超大观测网络及多GNSS系统的快速数据处理

Rapid Data Processing of Huge Networks and Multi-GNSS Constellation

  • 摘要: 目的 采用IGS全球约110个多模观测站4周的观测数据,在不同采样间隔下进行精密定轨数据处理。分析了不同采样间隔下产品的精度以及数据处理的耗时情况。大量计算结果表明:①随着数据采样间隔的增加,数据处理时间呈线性减少的趋势。本文表明,采用15min采样间隔比5min采样间隔计算效率最多可以提高50%以上。②数据采样间隔的变化对轨道、钟差、ERP参数、参考框架等解算参数的影响很小。当采样间隔为5~10min时,基本上没有影响。为分析不同采样间隔产品对用户定位的影响,采用了全球22个测站4周的数据进行PPP静态定位,并且采用 GRACE卫星1周的数据进行运动学精密定轨。采用不同轨道、钟差的静态结果表明,不同产品对水平方向精度的影响小于2mm,高程方向精度的影响小于6mm。GRACE卫星动态定位结果表明,不同产品对各个方向精度的影响小于1.5cm,三维位置的影响小于2cm。本文结论对于当前测站个数>250的非差数据处理有参考意义。

     

    Abstract: Objective In the first part of the paper we discuss the challenges of huge networks and multi-GNSS da-ta processing for the zero-difference(ZD)strategy.Using 4weeks’of data from global IGS GPS/GLONASS stations,we performed daily data processing with data sampling ranging from 5-15min.Acomparison of the processing time and product precision under different sampling data shows:①Computation efficiency is greatly improved by increasing data sampling;our results show the improve-ment of maximum 52%;② Difference of product precision was marginally observed,and product pre-cision is almost the same when the sampling rate was changed from 5-10min.To analyze the impact ofdifferent products on positioning applications,we performed PPP for 22globally distributed IGS sta-tions and kinematic precise orbit determination for GRACE satellites using products generated fromdifferent data sampling procedures.Results show:①Static PPP precision differs by less than 2mmand 6mm for the horizontal and height components,respectively;②kinematic PPP precision differsby less than 1.5cm for each coordinate component and less than 2cm in three-dimensions.

     

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