Train Posture Estimation Based on Kalman Fusion Algorithm

With the rapid development of science and technology, an accurate estimation of high-speed train motion posture forms the basis of attitude control. Safety is the primary prerequisite for train operation and the attitude of the train is an important parameter to evaluate the stability of the train....

Full description

Saved in:
Bibliographic Details
Published in2018 IEEE 1st International Conference on Micro/Nano Sensors for AI, Healthcare, and Robotics (NSENS) pp. 20 - 24
Main Authors Yip, Kingshing, Shien, Chengchun, Lin, Qiujun, Cui, Xiaole, Shi, Guangyi
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.12.2018
Subjects
Estimation
Jacobian matrices
Kalman filters
Mathematical model
Navigation
Noise measurement
Posture
Quaternions
Strapdown Inertial Navigation Technology
Online AccessGet full text

Cover

More Information
Summary:With the rapid development of science and technology, an accurate estimation of high-speed train motion posture forms the basis of attitude control. Safety is the primary prerequisite for train operation and the attitude of the train is an important parameter to evaluate the stability of the train. The accuracy of attitude depends not only on the performance and precision of the hardware configuration of the attitude measurement system, but also on the algorithm of attitude determination. This paper introduces strapdown inertial navigation technology and an attitude estimation algorithm based on the proposed Kalman fusion, in order to solve the problems of low accuracy and easy dispersing, associated with integrated navigation attitude estimations. According to the characteristics of train motion, the state equation and measurement equation are constructed to compensate for the errors in the inertial sensor gyroscope and accelerometer so that an accurate estimation of train posture is obtained.
DOI:10.1109/NSENS.2018.8713563