Accumulative Drifting Model of Inertial Navigation Platform under Elliptic Vibration

Abstract:

To facilitate the calibration of a precision inertial navigation platform, the drifting of the platform under vibratory testing environment is analyzed, and a simplified drift model is developed which features the accumulative rather than instantaneous impact of the vibration on the platform drifting. When applied to error parameter calibration for the platform, the proposed model entails much less computing load in drifting prediction, and removes the requirement of strict real-time synchronization between the vibration generating device and the drift-predicting programs. The form of vibration can be assumed to be elliptic, a relatively general one which allows the shaker to vibrate sinuoidally in two directions perpendicular to each other and with phase difference of 90 degree. Under certain circumstances, the elliptic vibration can be simplified to a linear or circular one, as is typical in practice. Simulations of the platform drifting error under linear, circular and general elliptic vibration shows that the accumulative model can well serve as an alternative to the conventional one in such test environments, and the merits of the proposed model become more prominent when the frequency of vibration gets higher.

• Periodical: Key Engineering Materials (Volume 613)
• Pages: 269-278
• DOI: https://doi.org/10.4028/www.scientific.net/KEM.613.269
• Online since: May 2014
• Authors: Zhen Xian Fu*, Yu Rong Lin, Yang Liu, Xing Lin Chen
• Keywords: Accumulative Model, Calibration, Drifting Error, Elliptic Vibration, Inertial Platform

References

[1] H.G. Wang, T.C. William, Strategic inertial navigation systems - high-accuracy inertially stabilized platforms for hostile environments, IEEE Control Systems Magazine, 20(2008) 65-85.

DOI: 10.1109/mcs.2007.910206

[2] Anthony Lawrence, Modern Inertial Technology, Springer, (2001).

[3] F.J. Hellings, Application of Extended Kalman Filtering to a Dynamic Laboratory Calibration of an Inertial Navigation System, AD-763718. (1976).

DOI: 10.21236/ad0763718

[4] N.S. Kumar, T. Jann, Estimation of attitudes from a low-cost miniaturized inertial platform using Kalman Filter-based sensor fusion algorithm, Sadhana. 29(2004) 217-235.

DOI: 10.1007/bf02703733

[5] M.S. Grewal, V.D. Henderson, and R.S. Miyasako, Application of Kalman Filtering to the Calibration and Alignment of Inertial Navigation Systems, IEEE Trans. on. Automatic Control, 36(1991) 3-13.

DOI: 10.1109/9.62283

[6] S. Julier, J. Uklmann, and H. Durrant-whyte, A new Methods for Nonlinear Transformation of Means and Covariances in Filters and Estimators, IEEE Trans. on Automatic Control, 45(2000) 477-482.

DOI: 10.1109/9.847726

[7] Z. Fu, Modeling and Error Identification for Inertial Platform, Doctoral Dissertation, Harbin Institute of Technology, 2002, pp.110-142.

[8] N. Barbour, Inertial sensor technology trends. IEEE Sensors Journal, 4(2001) 332-339.

[9] C.H. Barker, W.G. Rock, Advanced Inertial Test Laboratory: Improving Low-Noise Testing of High-Accuracy Instruments, AIAA-2009-1727. (2009).

DOI: 10.2514/6.2009-1727

[10] K.G. McConnell, Vibration Testing: Theory and Practice, Wiley-IEEE, (1995).