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A method of improving ambiguity fixing rate for post-processing kinematic GNSS data
Satellite Navigation ( IF 11.2 ) Pub Date : 2020-07-06 , DOI: 10.1186/s43020-020-00022-y
Xiaohong Zhang , Yuxi Zhang , Feng Zhu

Global Navigation Satellite System precise positioning using carrier phase measurements requires reliable ambiguity resolution. It is challenging to obtain continuous precise positions with a high ambiguity fixing rate under a wide range of dynamic scenes with a single base station, thus the positioning accuracy will be degraded seriously. The Forward–Backward Combination (FBC), a common post-processing smoothing method, is simply the weighted average of the positions of forward and backward filtering. When the ambiguity fixing rate of the one-way (forward or backward) filter is low, the FBC method usually cannot provide accurate and reliable positioning results. Consequently, this paper proposed a method to improve the accuracy of positions by integrating forward and backward AR, which combines the forward and backward ambiguities instead of positions—referred to as ambiguity domain-based integration (ADBI). The purpose of ADBI is to find a reliable correct integer ambiguities by making full use of the integer nature of ambiguities and integrating the ambiguities from the forward and backward filters. Once the integer ambiguities are determined correctly and reliably with ADBI, then the positions are updated with the fixing ambiguities constrained, in which more accurate positions with high confidence can be achieved. The effectiveness of the proposed approach is validated with airborne and car-borne dynamic experiments. The experimental results demonstrated that much better accuracy of position and higher ambiguity-fixed success rate can be achieved than the traditional post-processing method.

中文翻译:

一种改进的运动学GNSS数据歧义固定率的方法

全球导航卫星系统使用载波相位测量的精确定位需要可靠的歧义分辨率。在单个基站的大范围动态场景下,要获得具有高模糊度固定率的连续精确位置是一项挑战,因此定位精度将严重下降。前向后组合(FBC)是一种常见的后处理平滑方法,它只是前向和后向滤波位置的加权平均值。当单向(向前或向后)滤波器的模糊固定率较低时,FBC方法通常无法提供准确而可靠的定位结果。因此,本文提出了一种通过整合前后AR来提高位置精度的方法,它结合了前后歧义而不是位置,称为基于域的歧义集成(ADBI)。ADBI的目的是通过充分利用歧义的整数性质并整合前向和后向滤波器中的歧义来找到可靠的正确整数歧义。一旦使用ADBI正确,可靠地确定了整数模糊度,就可以在固定歧义度受到约束的情况下更新位置,从而可以以更高的置信度获得更准确的位置。机载和车载动态实验验证了该方法的有效性。实验结果表明,与传统的后处理方法相比,可以获得更高的位置精度和更高的歧义固定成功率。
更新日期:2020-07-06
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