基于自适应因子图的车载GNSS/INS组合导航方法  

Integrated navigation method for vehicle GNSS/INS based on adaptive factor graph

作　　者：赵海林 刘福朝 刘宁[1,2] 赵辉[1,2] 王桂奇[1] Zhao Hailin;Liu Fuchao;Liu Ning;Zhao Hui;Wang Guiqi(School of Automation,Beijing Information Science&Technology University,Beijing 100192,China;Beijing Key Laboratory of High Dynamic Navigation Technology,University of Beijing Information Science&Technology,Beijing 100101,China)

机构地区：[1]北京信息科技大学自动化学院,北京100192 [2]北京信息科技大学高动态导航技术北京市重点实验室,北京100101

出　　处：《电子测量技术》2024年第10期56-61,共6页Electronic Measurement Technology

基　　金：北京市自然科学基金(4244091);北京市教委科技一般项目(KM202311232015)资助。

摘　　要：针对短时遮蔽空间环境下,车载卫星导航系统多路径效应与信号衰减严重、粗差与周跳发生频繁,导致组合导航精度和鲁棒性降低的问题,提出了一种基于自适应因子图的车载GNSS/INS组合导航方法。首先构建GNSS/INS因子图模型,以INS为导航主系统,用GNSS因子对INS进行辅助修正,再根据GNSS测量的残差设计权重函数,进行自适应调整GNSS因子所占的权重,可有效抑制GNSS传感器随环境变化造成的发散误差,进而降低因GNSS定位误差过大对组合导航精度和鲁棒性的影响。最后通过搭载跑车实验进行对比验证,与传统基于因子图的组合导航方法相比,自适应因子图组合导航方法的定位均方根误差和最大误差分别降低了70.01%和55.31%。结果表明,所提方法定位精度更高、鲁棒性更好。In response to the significant challenges posed by multipath effects,signal attenuation,and frequent occurrences of gross errors and cycle slips in short-term occluded environments,which diminish the accuracy and robustness of vehicular satellite navigation systems,this paper proposes an integrated navigation method for vehicle GNSS/INS based on adaptive factor graph.Initially,a GNSS/INS factor graph model is established,utilizing INS as the primary navigation system,supplemented by GNSS factors for corrective adjustments based on GNSS measurement residuals.This design allows for adaptive adjustments to the weights of GNSS factors,effectively mitigating divergence errors caused by environmental changes and subsequently reducing the impact of excessive GNSS positioning errors on the combined navigation′s accuracy and robustness.The method was empirically validated using a racing car experiment,which demonstrated that the adaptive factor graph-based method significantly reduced the root mean square error and maximum error by 70.01%and 55.31%,respectively,compared to conventional factor graph-based methods.The results confirm that the proposed method enhances positioning accuracy and robustness.

关 键 词：组合导航 因子图 权重函数 自适应 

分 类 号：TN967.2[电子电信—信号与信息处理]