在分析传统GPS/GLONASS组合PPP数学模型中忽略GLONASS码IFB不足的基础上, 提出一种基于“多参数”的组合PPP与码IFB估计算法。将“频间偏差”与“系统时差”参数进行合并, 通过引入多个独立的“时频偏差”参数对组合PPP中的GLONASS码IFB进行函数模型补偿, 同时可实现基于单个测站观测数据的码IFB精确估计。对配备6种GNSS品牌接收机的30个IGS站实测数据进行GLONASS码IFB估计与分析。结果表明:各品牌接收机不同频率通道的GLONASS码IFB可达数米, 且表现出与频率的明显相关性, 但难以通过简单函数建模为其提供精确的先验改正值;相同品牌接收机的GLONASS码IFB整体上具有相似的特性, 而在个别测站会表现出异常特征;即使接收机类型、固件版本及天线类型完全相同的测站, GLONASS码IFB值也可能存在显著差异。新算法能实现对GLONASS码IFB的有效补偿, 明显加快组合PPP的收敛速度。虽然引入多个附加参数会导致函数模型自由度减小, 但对定位精度的影响有限, 与传统“单参数”法进行组合PPP的定位精度相当。
The disadvantages of ignoring GLONASS receiver code inter-frequency biases(IFBs) in GPS/GLONASS combined precise point positioning (PPP) are analysed in this contribution. A new algorithm of combined PPP and code IFBs estimation based on "multiple parameters" is proposed where inter-system bias parameter is merged with code IFB. Multiple independent inter-system and inter-frequency bias (ISFB) parameters are introduced to the observation equations which could compensate the GLONASS code IFBs in the function model. In the meantime, the GLONASS code IFBs can be estimated precisely based on a single station. GPS/GLONASS observation data from 30 IGS sites which involves 6 different GNSS receiver manufacturers is processed with the proposed algorithm. The results show that the GLONASS code IFBs could be several meters and a significant correlation exists between code IFB and signal frequency. It seems to be difficult to provide a priori code IFB precisely with simple function model. GLONASS code IFBs with the same receiver manufacturer mostly show similar characteristics, however, abnormal behaviors are also found in some receivers. It is worth to note that GLONASS code IFBs could be quite different with two receivers even if their receiver types, firmware versions and antenna types are all the same. The PPP results demonstrate that the new algorithm can significantly accelerate the convergence of combined PPP by compensating the GLONASS code IFBs efficiently. The combined PPP accuracy of "multiple parameters" method is comparable with that of traditional "single parameter" method and almost unaffected by freedom reduction of the function model.
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