TY - GEN
T1 - ANFIS-Based Seamless Train Positioning Method
AU - Chen, Yidi
AU - Jiang, Wei
AU - Wang, Jian
AU - Cai, Baigen
AU - Jiang, Yiping
N1 - Funding Information:
*Resrach supported by National Natural Science Foundation of China "Joint Fund Project" under Grant u1934222.
Publisher Copyright:
© 2022 IEEE.
PY - 2022/10
Y1 - 2022/10
N2 - GNSS/INS integrated navigation system is the development trend of railway satellite positioning. However, there are many dense forests, mountains and tunnels in the railway environment, which will affect the satellite signals during the operation of the train, so that the positioning error of the INS rapidly diverges, and eventually the entire system fails. In order to solve this problem, this paper combines ANFIS with strong self-learning ability with GNSS/INS, and proposes a seamless train positioning method based on self-learning (GNSS/ANFIS/INS). The above method is applied to the Shuozhou-Huanghua railway, and the analysis of the test results shows that the method can effectively reduce the positioning error and velocity error in the case of GNSS failure. Compared with only INS, the RMSE of position and velocity are reduced by approximately 85% and 75%, respectively.
AB - GNSS/INS integrated navigation system is the development trend of railway satellite positioning. However, there are many dense forests, mountains and tunnels in the railway environment, which will affect the satellite signals during the operation of the train, so that the positioning error of the INS rapidly diverges, and eventually the entire system fails. In order to solve this problem, this paper combines ANFIS with strong self-learning ability with GNSS/INS, and proposes a seamless train positioning method based on self-learning (GNSS/ANFIS/INS). The above method is applied to the Shuozhou-Huanghua railway, and the analysis of the test results shows that the method can effectively reduce the positioning error and velocity error in the case of GNSS failure. Compared with only INS, the RMSE of position and velocity are reduced by approximately 85% and 75%, respectively.
UR - http://www.scopus.com/inward/record.url?scp=85141842005&partnerID=8YFLogxK
U2 - 10.1109/ITSC55140.2022.9921885
DO - 10.1109/ITSC55140.2022.9921885
M3 - Conference article published in proceeding or book
AN - SCOPUS:85141842005
T3 - IEEE Conference on Intelligent Transportation Systems, Proceedings, ITSC
SP - 1856
EP - 1861
BT - 2022 IEEE 25th International Conference on Intelligent Transportation Systems, ITSC 2022
PB - Institute of Electrical and Electronics Engineers Inc.
ER -