A fast continuous self-calibration method for FOG rotational inertial navigation system based on invariant extended Kalman filter

• Published in: IEEE sensors journal Vol. 23; no. 3; p. 1
• Main Authors: Li, Jun, Zhang, Shifeng, Yang, Huabo, Jiang, Zhenyu, Bai, Xibin
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.02.2023; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Accelerometers; Algorithms; Alignment; Attitudes; Calibration; Convergence; Errors; Extended Kalman filter; Gyroscopes; Inertial navigation; invariant extended Kalman filter; Invariants; Lie groups; Navigation systems; Nonlinear systems; Rotation; rotational inertial navigation system; Self calibration; self-alignment
• DOI: 10.1109/JSEN.2022.3226327
• ISSN: 1530-437X

For a given device, the rapidity and accuracy of device error coefficient calibration and attitude alignment of the rotational inertial navigation system mainly depend on the convergence rate of the nonlinear filtering algorithm. The traditional extended Kalman filter method has no theoretical convergence guarantee for non-linear systems and requires a long time in the actual calibration process. Since the attitude is subject to the 3-dimensional rotation transformation group, the error transfer equation can be better linearized by defining the error on the lie group to use the new observation information fully. This paper proposes a primary method of using the invariant extended Kalman filter to carry out an inertial navigation system. In the continuous rotation of the framework, the accelerometer, gyroscope, and framework angle are used to calibrate the error coefficient of the accelerometer and gyroscope and estimate the base alignment error Angle. Simulation and experiment results show that the proposed method can accomplish alignment and calibration in 10 minutes under a large initial attitude error.