一种BDS非差非组合PPP中电离层功率谱密度估计方法

A Method for Estimating Ionospheric Power Spectral Density in BDS Undifferenced and Uncombined PPP

  • 摘要: 无电离层组合模型和非差非组合模型是精密单点定位(precise point positioning,PPP)中最常用的两种函数模型。非差非组合模型中电离层误差常被描述为随机游走,随机游走过程中的功率谱密度成为决定PPP定位性能的主要因素,采用经验值功率谱密度的方法没有考虑电离层小尺度变化。在非差非组合模型的基础上,分析电离层时间相关性信息,在电离层差分时间间隔较小时,观测噪声较大甚至淹没电离层的变化。因此,通过平滑去噪的方法削弱观测值噪声的影响,实时确定电离层功率谱密度,对非差非组合模型中的电离层延迟参数进行合理约束,从而改善定位性能。对12个测站10 d的北斗卫星导航系统(BeiDou satellite navigation system,BDS)数据进行不同电离层模型下的解算,结果表明:相对于传统无电离层组合PPP模型,所提方法在收敛时间上缩短约8.2%,水平方向精度相当,垂直方向定位精度提高约31%。相较于功率谱密度采用经验值方法,所提方法在收敛时间上缩短约9.7%,水平方向精度相当,垂直方向定位精度提高约31%。

     

    Abstract:
    Objectives The ionosphere-free (IF) model and the undifferenced and uncombined (UDUC) model are the two most commonly used functional models in precise point positioning (PPP). In the UDUC model, the ionospheric error is often described as random walk parameter , and the power spectral density (PSD) in the process of random walk becomes the main factor determining the positioning performance of PPP. The method of determining the PSD by the empirical value can not show the small scale variation of the ionosphere.
    Methods Based on the UDUC model, the time correlation information of the ionosphere is analyzed. When the difference time interval of the ionosphere is small, the observation noise is large and it will submerge the changes of the ionosphere.Hence,we use the smooth denoising method to weaken the influence of the observation noise, determine the ionospheric PSD in real time, and reasonably constrain the ionospheric delay parameters in the UDUC model, so as to improve the positioning performance.
    Results The experiment was carried out through 10 d BeiDou satellite navigation system (BDS) observations come from 12 stations under different ionospheric models. The results show that compared with the traditional ionospheric-free model, the convergence time of the proposed method is shortened by 8.2%, the horizontal direction accuracy is equivalent, and the vertical direction positioning accuracy is improved by about 31%. Compared with the empirical value PSD method,the convergence time of the proposed method is shortened by about 9.7%, the horizontal direction accuracy is equivalent, and the vertical direction positioning accuracy is improved by about 31%.
    Conclusions We recommend that BDS users adopt our proposed method when using UDUC model, which can effectively shorten the convergence time and improve the positioning accuracy to a certain extent.

     

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