摘要
针对传统基于SINS/BDS组合的列车组合导航系统过程中因列车运行环境复杂多变导致传感器运行状态存在随机干扰的特点,为提高系统的可靠性,降低引入的不确定噪声对组合导航精度的影响,提出一种基于双因子抗差估计的SINS/BDS组合算法模型,在传统M估计基础上,引入系统观测模型不符因子,降低观测模型和动力学模型误差对组合导航精度的影响。为解决因BDS数据延时以及速度位置更新解算造成的延时误差问题,在抗差更新模型中引入延时估计误差。通过仿真与车载实验表明,双因子抗差估计模型可以有效提高组合导航系统的鲁棒性,延时估计能有效降低组合更新以及数据延时误差,降低复杂环境的随机噪声对列车定位精度的影响。
In view of the characteristics of random interference in the operation of the sensors of traditional SINS/BDS-based integrated train navigation system due to the complex and changeable train operating environment,in order to improve the reliability of the system and reduce the impact of uncertain noise on the accuracy of integrated navigation,a SINS/BDS integrated algorithm model based on dual-factor robust estimation was proposed.Based on traditional M-estimation,the inconsistency factor of system observation model was introduced to reduce the influence of the errors of the observation model and dynamic model on the accuracy of the integrated navigation.In order to solve the problem of delay error caused by BDS data delay and velocity position update,a delay estimation error was introduced into robust update model.Simulation and on-board experiments show that the two-factor robust estimation model can effectively improve the robustness of integrated navigation system,while the delay estimation can effectively reduce the combination update and data delay errors,and reduce the impact of random noise in complex environment on train positioning accuracy.
作者
何浩洋
夏荣斌
HE Haoyang;XIA Rongbin(Nanjing NRIET Industrial Co.,Ltd.,Nanjing 211100,China;Changzhou Research Institute,Lanzhou Jiaotong University,Changzhou 213000,China)
出处
《铁道学报》
EI
CAS
CSCD
北大核心
2022年第8期50-59,共10页
Journal of the China Railway Society
