Robust train localisation method based on advanced map matching measurement-augmented tightly-coupled GNSS/INS with error-state UKF

被引:1
|
作者
Liu, Dan [1 ]
Jiang, Wei [1 ]
Cai, Baigen [1 ]
Heirich, Oliver [2 ]
Wang, Jian [1 ]
Wei, Shangguan [1 ]
机构
[1] Beijing Jiaotong Univ, Sch Elect & Informat Engn, Beijing, Peoples R China
[2] German Aerosp Ctr, Inst Commun & Nav, Oberpfaffenhofen, Germany
来源
JOURNAL OF NAVIGATION | 2023年 / 76卷 / 2-3期
基金
中国国家自然科学基金;
关键词
Robust estimation; GNSS; inertial navigation system (INS); unscented Kalman filter; map;
D O I
10.1017/S0373463323000097
中图分类号
U6 [水路运输]; P75 [海洋工程];
学科分类号
0814 ; 081505 ; 0824 ; 082401 ;
摘要
This paper presents a robust train localisation system by fusing a Global Navigation Satellite System (GNSS) with an Inertial Navigation System (INS) in a tightly-coupled (TC) strategy. To improve navigation performance in GNSS partly blocked areas, an advanced map-matching (MM) measurement-augmented TC GNSS/INS method is proposed via an error-state unscented Kalman filter (UKF). The advanced MM generates a matched position using a one-step predicted position from a UKF time update step with binary search algorithm and a point-line projection algorithm. The matched position inputs as an additional measurement to fuse with the INS position to augment the degraded GNSS pseudorange measurement to optimise the state estimation in the UKF measurement update step. Both the real train test on the Qinghai-Tibet railway and the simulation were carried out and the results confirm that the proposed advanced MM measurement-augmented TC GNSS/INS with error-state UKF provides the best horizontal positioning accuracy of 0 center dot 67 m, which performs an improvement of about 71% and 90% with respect to TC GNSS/INS with only error-state UKF and only error-state Extended Kalman filter in GNSS partly blocked areas.
引用
收藏
页码:316 / 339
页数:24
相关论文
共 1 条
  • [1] Train Localization Method for Tightly-coupled GNSS/INS Integrated Strategy Based on Simplified Robust UKF
    Liu D.
    Jiang W.
    Cai B.
    Wang J.
    Shangguan W.
    Tiedao Xuebao/Journal of the China Railway Society, 2023, 45 (07): : 62 - 71