首页|一种智能手机GNSS精密定位随机模型构建方法及定位性能分析

一种智能手机GNSS精密定位随机模型构建方法及定位性能分析

A stochastic model construction method for smartphone GNSS precision positioning and analysis of positioning performance

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全球导航卫星系统(GNSS)定位芯片已经成为智能手机中实现导航定位的重要传感器,随着信息化、智能化社会的不断发展和深入,人们对手机高精度定位服务的需求不断增加.在分析伪距残差与高度角、载噪比(C/N0)及手机GNSS原始观测信息的相关性时发现:相比于C/N0,手机GNSS原始观测信息中的ReceivedSvTimeUncertaintyNanos与伪距残差的相关性更高.因此,提出了一种基于岭回归和智能手机原始观测信息的GNSS精密定位随机模型构建方法.通过构建以手机原始观测信息为特征变量的岭回归模型,利用谷歌公开数据集训练该模型,学习伪距残差与各种特征变量之间的映射关系,进而在线预测伪距噪声,以此作为GNSS定位中观测值定权的依据.最后,进行实时动态定位(RTK)和精密单点定位(PPP)实验以验证新定权方法的定位性能,实验结果表明,相比传统C/N0定权方法,新定权方法的定位精度在3组实验中平均提升幅度分别为 24.9％,44.8％及 39.3％.
The global navigation satellite system(GNSS)positioning chip has become an important sensor for navigation and positioning with smartphones.With the continuous development and deepening of the information and intelligent society,the demand for high-precision positioning services for mobile phones has been increasing.In this paper,by analyzing the correlation between pseudorange residuals and altitude angle,C/N0,and original GNSS observation information of mobile phones,it is found that the ReceivedSvTimeUncertaintyNanos in the original GNSS obser-vation information of mobile phones has a higher correlation with pseudorange residuals compared with C/N0.Therefore,a stochastic model construction method for GNSS precision positioning based on ridge regression and raw observation information of smartphones is proposed.By con-structing a ridge regression model with the original mobile phone observation information as the feature variables,training the model with the Google open dataset,the learning of the relationship between the pseudorange residuals and various feature variables is performed,and then predicting the pseudorange noise online,which is used as the basis for weighting observations in GNSS posi-tioning.Finally,real-time kinematic positioning(RTK)and precise point positioning(PPP)ex-periments are carried out to verify the positioning performance of the new weighting method,and the experimental results show that the positioning accuracy of the new weighting method is im-proved by an average of 24.9％,44.8％,and 39.3％,respectively,compared with that of the tra-ditional C/N0 weighting method in the three sets of experiments.

Stochastic modelRidge regressionGlobal navigation satellite system(GNSS)Weighting methodReal-time kinematicPrecise point positioning

冯家昊、任晓东、张小红、陈刘成、王腾

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武汉大学测绘学院,武汉 430079

湖北珞珈实验室,武汉 430079

武汉大学中国南极测绘研究中心,武汉 430079

中国星网网络系统研究院有限公司,北京 100029

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随机模型 岭回归 GNSS 定权方法 实时动态定位 精密单点定位

国家重点研发计划项目国家自然科学基金国家自然科学基金国家自然科学基金

2022YFB3903902422301044242500342174031

2024

10.19306/j.cnki.2095-8110.2024.05.012

导航定位与授时

导航定位与授时

CSTPCD
ISSN：
年,卷(期)：2024.11(5)
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