测试科学与仪器2025，Vol.16Issue(3)：406-414,9.DOI:10.62756/jmsi.1674-8042.2025039
复杂运行场景下的BDS/SINS列车组合定位方法
A BDS/SINS integrated positioning approach for trains in complicated operation scenes
摘要
Abstract
The traditional train positioning methods suffer from inadequate accuracy and high maintenance costs,rendering them unsuitable for the development requirements of lightweight and intelligent train positioning technology.To address these restraints,the BeiDou navigation satellite system/strapdown inertial navigation system(BDS/SINS)integrated train positioning system based on an adaptive unscented Kalman filter(AUKF)is proposed.Firstly,the combined denoising algorithm(CDA)and Lagrange interpolation algorithm are introduced to preprocess the original data,effectively eliminating the influence of noise signals and abnormal measurements on the train positioning system.Secondly,the innovation theory is incorporated into the unscented Kalman filter(UKF)to derive the AUKF,which accomplishes an adaptive update of the measurement noise covariance.Finally,the positioning performance of the proposed AUKF is contrasted with that of conventional algorithms in various operation scenes.Simulation results demonstrate that the average value of error calculated by AUKF is less than 1.5 m,and the success rate of positioning touches 95.0%.Compared to Kalman filter(KF)and UKF,AUKF exhibits superior accuracy and stability in train positioning.Consequently,the proposed AUKF is well-suited for providing precise positioning services in variable operating environments for trains.关键词
列车组合定位/北斗卫星导航系统/捷联惯性导航系统/拉格朗日插值法/联合去噪算法/自适应无迹卡尔曼滤波Key words
train integrated positioning/BeiDou navigation satellite system(BDS)/strapdown inertial navigation system(SINS)/Lagrange interpolation algorithm/combined denoising algorithm(CDA)/adaptive unscented Kalman filter(AUKF)引用本文复制引用
武晓春,杨伟康..复杂运行场景下的BDS/SINS列车组合定位方法[J].测试科学与仪器,2025,16(3):406-414,9.基金项目
This work was supported by Project Fund of China National Railway Group Co.,Ltd.(No.N2022G012),and Natonal Natural Science Foundation of China(No.61661027) （No.N2022G012）
