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Multipath extraction and mitigation for static relative positioning based on adaptive layer wavelet packets, bootstrapped searches and CNR constraints
GPS Solutions ( IF 4.9 ) Pub Date : 2021-07-02 , DOI: 10.1007/s10291-021-01160-9
Mingkun Su 1 , Lei Qiao 1 , Hao Ma 1 , WeiJun Feng 1 , Zhaoyang Qiu 1 , Yanxi Yang 2 , Jiansheng Zheng 3
Affiliation  

Multipath is one of the major error sources in high-precision GNSS positioning since it cannot be mitigated by double differences or corrected by empirical models. Considering that the multipath error is related to the carrier-to-noise ratio (CNR) of the signal strength, an enhanced multipath extraction and mitigation method based on an adaptive layer wavelet packets, bootstrapped searches strategy and CNR constraints is proposed. The key concept of the proposed method is to use the adaptive layer-selecting wavelet packets transform to improve the precision of the multipath correction model, which is extracted from the reference day. In addition, to improve the accuracy and effectiveness of multipath mitigation on the subsequent observation day, a bootstrap search strategy based on CNR constraints is adopted. Real data sets are collected to assess the performance of the denoising and the static relative positioning of the proposed method; experimental results show that: (1) the multipath residuals of the carrier phase maintain a strong relationship with the CNR. Thus, the proposed method based on CNR constraints is feasible. Moreover, based on analysis of the distribution of multipath residuals, it can be found that constant layer wavelet packets transform denoising can not only lead to inefficiency for most epochs with low residuals but can also reduce the effectiveness of denoising for epochs with large residuals. (2) The average improvement rate of the root mean square (RMS) of the single-difference residuals after adopting the proposed method can reach approximately 25.33% compared with the original residuals and approximately 10.37% compared with the constant layering method, which indicates that the proposed method can improve the accuracy of the multipath correction model effectively; (3) For the positioning results, after applying the proposed method, the RMS of bias can improve 30.77, 31.25 and 38.20% in the east, north and up components compared with the original result. Even compared with the constant layering multipath mitigation method, the improvement rate can also reach approximately 29.79% for 3D positioning. It is worth noting that this proposed method is also suitable for other GNSS static relative positioning applications such as BDS and Galileo.



中文翻译:

基于自适应层小波包、自举搜索和 CNR 约束的静态相对定位多径提取和缓解

多径是高精度 GNSS 定位中的主要误差源之一,因为它无法通过双重差异来缓解或通过经验模型进行校正。考虑到多径误差与信号强度的载噪比(CNR)有关,提出了一种基于自适应层小波包、自举搜索策略和CNR约束的增强型多径提取和抑制方法。该方法的关键概念是利用自适应层选择小波包变换来提高多径校正模型的精度,该模型是从参考日中提取的。此外,为了提高后续观测日多径缓解的准确性和有效性,采用基于 CNR 约束的引导搜索策略。收集真实数据集以评估去噪性能和所提出方法的静态相对定位;实验结果表明:(1)载波相位的多径残差与CNR保持很强的相关性。因此,所提出的基于CNR约束的方法是可行的。此外,基于对多径残差分布的分析,可以发现恒定层小波包变换去噪不仅会导致大多数低残差时期的低效率,而且还会降低残差大时期去噪的有效性。(2)采用所提方法后单差残差的均方根(RMS)平均改善率较原始残差可达到约25.33%,约为10。与恒定分层法相比提高了37%,表明该方法可以有效提高多径校正模型的精度;(3) 对于定位结果,应用该方法后,东、北、上分量的偏差均方根值比原始结果分别提高了30.77%、31.25%和38.20%。即使与恒定分层多径缓解方法相比,3D定位的改进率也可以达到约29.79%。值得注意的是,该方法也适用于其他GNSS静态相对定位应用,如BDS和Galileo。与原始结果相比,东、北和上分量的偏差均方根值可提高 30.77、31.25 和 38.20%。即使与恒定分层多径缓解方法相比,3D定位的改进率也可以达到约29.79%。值得注意的是,该方法也适用于其他GNSS静态相对定位应用,如BDS和Galileo。与原始结果相比,东、北和上分量的偏差均方根值可提高 30.77、31.25 和 38.20%。即使与恒定分层多径缓解方法相比,3D定位的改进率也可以达到约29.79%。值得注意的是,该方法也适用于其他GNSS静态相对定位应用,如BDS和Galileo。

更新日期:2021-07-04
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