城市分类场景的GNSS伪距随机模型构建及其定位性能分析  

GNSS Pseudorange Stochastic Model for Urban Scenes Classification and Its Positioning Performance Analysis

作　　者：李岚 朱锋[1] 刘万科[1] 张小红[1,3] LI Lan;ZHU Feng;LIU Wanke;ZHANG Xiaohong(School of Geodesy and Geomatics,Wuhan University,Wuhan 430079,China;JinWei Integrated Circuit Co.Ltd,Changsha 410000,China;Chinese Antarctic Center of Surveying and Mapping,Wuhan University,Wuhan 430079,China)

机构地区：[1]武汉大学测绘学院,湖北武汉430079 [2]长沙金维集成电路股份有限公司,湖南长沙410000 [3]武汉大学中国南极测绘研究中心,湖北武汉430079

出　　处：《武汉大学学报(信息科学版)》2025年第3期545-553,共9页Geomatics and Information Science of Wuhan University

基　　金：国家重点研发计划(2020YFB0505803);国家自然科学基金(42104021);湖北省科技重大项目(2021AAA010)。

摘　　要：城市复杂场景容易引起全球导航卫星系统(global navigation satellite system,GNSS)信号出现中断、衰减、多径和非视距严重等问题,难以保证GNSS定位服务的可用性、连续性与可靠性。为提高城市复杂场景下的GNSS定位性能,提出了一种精细构建城市分类场景GNSS随机模型的方法,利用高精度组合导航设备提供动态参考基准实现伪距误差精确提取,通过分析不同城市场景下的GNSS信号特征与影响因素,建立了分场景随机模型。实际车载测试表明,分场景随机模型能有效减弱部分定位粗差的影响,相比于经典高度角随机模型,水平、垂直定位精度分别提升16.76%、16.18%;相比经典信噪比随机模型,水平、垂直定位精度分别提升18.68%、17.72%。所提方法为实现复杂场景下随机模型的弹性优化提供了新思路。Objective:Harsh urban contexts may cause positioning problems,such as interruption,attenuation,and serious multipath error.It's difficult to ensure the availability,continuity and reliability of GNSS positioning services.To improve GNSS positioning performance in complex urban contexts,this paper proposes a method for constructing GNSS stochastic models adapting to different urban contexts.Methods:First,GNSS signal characteristics in different contexts are analyzed to reveal the significant discrepancy of GNSS signals in varied contexts.Then,dynamic reference benchmarks provided by high-precision integrated navigation equipment are used to extract pseudorange error accurately.In addition,appropriate error statistic(median)and impact factor(C/N0)are selected after tests.Finally,the GNSS stochastic models adapting to different urban contexts are constructed using C/N0 and pseudorange error.Results:The urban vehicle test shows that the stochastic model adapting to different urban contexts can effectively weaken the influence of some gross errors.Compared to elevation stochastic model,the positioning accuracy is im proved by 16.76% and 16.18% in horizontal and vertical directions,and by 18.68% and 17.72% compared to C/N0 stochastic model.Conclusions:Stochastic model reconstructed adapting to different environments can weight observations more realistic,thus improving GNSS positioning performance.It provides a new idea for resilient optimization of stochastic models in complex contexts.

关 键 词：GNSS随机模型 伪距误差提取 分类场景 

分 类 号：P228[天文地球—大地测量学与测量工程]