A new continuous self-calibration scheme for a gimbaled inertial measurement unit

A typical calibration scheme for a gimbaled inertial measurement unit (IMU) involves an estimation of error parameters of an IMU mounted on an inertial platform and the platform's misalignment angles. However, traditional calibration methods for the gimbaled IMU have some serious defects. The excitation for a gyro's scale factors and misalignment angles is only the Earth rate in multi-position calibration methods and dynamic errors (unneeded motion of gyro floaters) involved in a continuous calibration process. This paper presents a new continuous self-calibration scheme for the gimbaled IMU. By processing the multi-position and continuous rotation steps alternately, the dynamic errors are suppressed and the excitation is augmented. This is more effective than traditional methods. Additionally, the platform rotation trajectory is designed to provide adequate observability for all parameters through a new methodology. The Lie derivative is used to compute the observability, and the genetic algorithm is utilized to obtain the inertial platform's optimal rotation trajectory based on the measurement of observability for all parameters. Simulation results show that the error coefficients can be effectively calibrated within an hour by the proposed scheme, and it is of high significance for fast launching of missiles and rockets.

[1]  Frank N. Barnes,et al.  Stable Member Equations of Motion for a Three-Axis Gyro Stabilized Platform , 1971, IEEE Transactions on Aerospace and Electronic Systems.

[2]  Xinlong Wang,et al.  An intelligentized and fast calibration method of SINS on moving base for planed missiles , 2009 .

[3]  M. S. Grewal,et al.  Application of Kalman filtering to the calibration and alignment of inertial navigation systems , 1991 .

[4]  Naser El-Sheimy,et al.  A new multi-position calibration method for MEMS inertial navigation systems , 2007 .

[5]  G. Zhu,et al.  An Extended Luenberger-Like Observer and its Application to Target Tracking , 2005 .

[6]  A. Isidori.  Nonlinear Control Systems , 1985 .

[7]  Andrew Y. C. Nee,et al.  Methods for in-field user calibration of an inertial measurement unit without external equipment , 2008 .

[8]  Arthur Gelb,et al.  Applied Optimal Estimation , 1974 .

[9]  Richard A. Kitzerow.  Design, Development, Analysis, and Laboratory Test Results of a Kalman Filter System-Level IMU Calibration Technique , 1977 .

[10]  Yuanxin Wu,et al.  Improved multi-position calibration for inertial measurement units , 2009 .

[11]  I. Bar-Itzhack,et al.  Observability analysis of piece-wise constant systems. II. Application to inertial navigation in-flight alignment (military applications) , 1992 .

[12]  A. Jackson,et al.  Continuous calibration and alignment techniques for an all-attitude inertial platform , 1973 .

[13]  Der-Ren Taur,et al.  A composite guidance strategy for SAAMM with side jet controls , 2001 .

[14]  I. Bar-Itzhack,et al.  Observability analysis of piece-wise constant systems with application to inertial navigation , 1990, 29th IEEE Conference on Decision and Control.