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Mitigating BeiDou Satellite-Induced Code Bias: Taking into Account the Stochastic Model of Corrections

机译:减轻北斗卫星引起的代码偏差:考虑到校正的随机模型

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The BeiDou satellite-induced code biases have been confirmed to be orbit type-, frequency-, and elevation-dependent. Such code-phase divergences (code bias variations) severely affect absolute precise applications which use code measurements. To reduce their adverse effects, an improved correction model is proposed in this paper. Different from the model proposed by Wanninger and Beer (2015), more datasets (a time span of almost two years) were used to produce the correction values. More importantly, the stochastic information, i.e. , the precision indexes, were given together with correction values in the improved model. However, only correction values were given while the precision indexes were completely missing in the traditional model. With the improved correction model, users may have a better understanding of their corrections, especially the uncertainty of corrections. Thus, it is helpful for refining the stochastic model of code observations. Validation tests in precise point positioning (PPP) reveal that a proper stochastic model is critical. The actual precision of the corrected code observations can be reflected in a more objective manner if the stochastic model of the corrections is taken into account. As a consequence, PPP solutions with the improved model outperforms the traditional one in terms of positioning accuracy, as well as convergence speed. In addition, the Melbourne-Wübbena (MW) combination which serves for ambiguity fixing were verified as well. The uncorrected MW values show strong systematic variations with an amplitude of half a wide-lane cycle, which prevents precise ambiguity determination and successful ambiguity resolution. After application of the code bias correction models, the systematic variations can be greatly removed, and the resulting wide lane ambiguities are more likely to be fixed. Moreover, the code residuals show more reasonable distributions after code bias corrections with either the traditional or the improved model.
机译:北斗卫星引起的代码偏差已被证实与轨道类型,频率和仰角有关。这种代码相位差异(代码偏差变化)会严重影响使用代码测量的绝对精确应用。为了减少其不利影响,本文提出了一种改进的校正模型。与Wanninger和Beer(2015)提出的模型不同,更多的数据集(将近两年的时间)用于生成校正值。更重要的是,在改进的模型中,给出了随机信息,即精度指标,以及校正值。但是,仅给出校正值,而传统模型中的精度指标却完全缺失。使用改进的校正模型,用户可以更好地了解其校正,尤其是校正的不确定性。因此,这有助于完善代码观察的随机模型。精确点定位(PPP)中的验证测试表明,正确的随机模型至关重要。如果考虑到校正的随机模型,则可以更客观的方式反映出校正后的代码观测值的实际精度。因此,具有改进模型的PPP解决方案在定位精度和收敛速度方面均优于传统解决方案。此外,还验证了用于解决歧义的Melbourne-Wübbena(MW)组合。未校正的MW值显示出很强的系统变化,幅度为宽通道周期的一半,这会阻止精确的歧义确定和成功的歧义解决。应用代码偏差校正模型后,可以大大消除系统差异,并且更可能解决宽车道歧义问题。此外,在使用传统模型或改进模型进行代码偏差校正之后,代码残差显示出更合理的分布。

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