Mitigating BeiDou Satellite-Induced Code Bias: Taking into Account the Stochastic Model of Corrections
AbstractThe 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. View Full-Text
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Guo, F.; Li, X.; Liu, W. Mitigating BeiDou Satellite-Induced Code Bias: Taking into Account the Stochastic Model of Corrections. Sensors 2016, 16, 909.
Guo F, Li X, Liu W. Mitigating BeiDou Satellite-Induced Code Bias: Taking into Account the Stochastic Model of Corrections. Sensors. 2016; 16(6):909.Chicago/Turabian Style
Guo, Fei; Li, Xin; Liu, Wanke. 2016. "Mitigating BeiDou Satellite-Induced Code Bias: Taking into Account the Stochastic Model of Corrections." Sensors 16, no. 6: 909.
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