TY - GEN
T1 - An online virtual gyroscope technique using convolutional neural network
AU - Liu, Cong
AU - Li, Huaijian
AU - Du, Xiaojing
AU - Chen, Zhaoyi
AU - Liu, Yang
AU - Yan, Junliang
N1 - Publisher Copyright:
© 2018 IEEE.
PY - 2018/10/5
Y1 - 2018/10/5
N2 - Inertial device is the core of the SINS and the gyroscope senses the angular velocity of the carrier. The failure of the gyroscope causes the navigation system to be unable to resolve the attitude of the carrier. In order to improve the fault-tolerance of SINS, this paper proposes an online virtual gyroscope algorithm based on convolutional neural network using IMU's own real-time data and analyzes the feasibility of online virtual algorithm. First, the equations for calculating the angular velocity of the carrier using the information contained in the accelerometer are analyzed to determine the input data and output data of the convolutional neural network. Then, the online training convolutional neural network model is established, and a four-layer neural network with two convolutional layers, one fully connected layer, and an output layer is proposed as the predicting model. Finally, the feasibility of the proposed virtual algorithm is verified by mathematical simulation.
AB - Inertial device is the core of the SINS and the gyroscope senses the angular velocity of the carrier. The failure of the gyroscope causes the navigation system to be unable to resolve the attitude of the carrier. In order to improve the fault-tolerance of SINS, this paper proposes an online virtual gyroscope algorithm based on convolutional neural network using IMU's own real-time data and analyzes the feasibility of online virtual algorithm. First, the equations for calculating the angular velocity of the carrier using the information contained in the accelerometer are analyzed to determine the input data and output data of the convolutional neural network. Then, the online training convolutional neural network model is established, and a four-layer neural network with two convolutional layers, one fully connected layer, and an output layer is proposed as the predicting model. Finally, the feasibility of the proposed virtual algorithm is verified by mathematical simulation.
KW - Convolutional neural network
KW - Gyroscope
KW - Online virtual
KW - Strapdown inertial navigation system
UR - https://www.scopus.com/pages/publications/85056333599
U2 - 10.1109/ICMA.2018.8484406
DO - 10.1109/ICMA.2018.8484406
M3 - Conference contribution
AN - SCOPUS:85056333599
T3 - Proceedings of 2018 IEEE International Conference on Mechatronics and Automation, ICMA 2018
SP - 358
EP - 363
BT - Proceedings of 2018 IEEE International Conference on Mechatronics and Automation, ICMA 2018
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 15th IEEE International Conference on Mechatronics and Automation, ICMA 2018
Y2 - 5 August 2018 through 8 August 2018
ER -