We found a match
Your institution may have access to this item. Find your institution then sign in to continue.
- Title
基于因子图的 BDS/IMU 列车 定位信息融合模型.
- Authors
王运明; 程相; 李卫东; 初宪武
- Abstract
Accurate train position information is the key to ensure the high-speed and safe operation of the train. The traditional information fusion method has larger error in the Beidou Navigation Satellite System (BDS)/ Inertial Measurement Unit (IMU) train integrated positioning model, which leads to the low accuracy of train positioning. A BDS/IMU train positioning information fusion model based on factor graph was proposed. Using the factor graph theory, the measurement information received by BDS and IMU sensors was abstracted as factor nodes. The state information was abstracted as variable nodes. The BDS/IMU train positioning information fusion factor graph model was constructed. When BDS received effective signals, it only needed to add factor nodes at a specific time in the factor graph to realize the plug and play of positioning sensors. The joint probability density function with BDS/IMU train positioning status information and measurement information as variables was defined in the model. According to the nonlinear optimization theory, it was linearized through Taylor expansion and transformed into a standard least squares problem. The Gaussian-Newton iterative factor graph inference algorithm is designed to solve the maximum of a posteriori estimation of the joint probability distribution function and calculate the optimal estimation value of BDS/IMU train positioning information. To get the accurate position information of the train. Through the simulated data, the verification of the model shows that the factor graph model effectively reduces the position error and speed error of the train compared with the Kalman algorithm and no error divergence. Thus it effectively realizes the unequal interval fusion of different train positioning sensors, At the same time, it enhances the information fusion ability of train positioning and improves the accuracy of train positioning. Under the measured data, the root means square error of train positioning position is reduced to less than 2 m, which is significantly less than Kalman algorithm. It can provide accurate position information for high-speed and reliable operation of the train.
- Subjects
BEIDOU satellite navigation system; STANDARD deviations; DISTRIBUTION (Probability theory); GRAPH theory; PROBABILITY density function; HIGH speed trains; AUTOMOBILE speed
- Publication
Journal of Railway Science & Engineering, 2023, Vol 20, Issue 3, p1077
- ISSN
1672-7029
- Publication type
Article
- DOI
10.19713/j.cnki.43-1423/u.T20220672