Study on SRUKF Applied in Initial Alignment with Large Misalignment Angle on Stationary Base of SINS

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Abstract:

The initial alignment error model of SINS (Strap-down Inertial Navigation System) with large misalignment angle is nonlinear. The traditional EKF (Extended Kalman Filter) was used to linearization a nonlinear system, but its performance is limited. In this paper we use the SRUKF (Square Root Unscented Kalman Filter) to process this nonlinear system and the results indicate that SRUKF is better than EKF in convergence speed and estimation accuracy.

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Advanced Materials Research (Volumes 383-390)

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5088-5093

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November 2011

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© 2012 Trans Tech Publications Ltd. All Rights Reserved

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[1] R.E. Kalman, A new approach to linear filtering and prediction problems, Transactions of the ASME-Journal of Basic Engineering, 82(Series D): 35–45, (1960).

DOI: 10.1115/1.3662552

Google Scholar

[2] Qin Yongyuan, Zhang Hongyue and Wang Shuhua, The Principle of Kalman Filter and Navigation System [M], Xi'an: Northwestern Polytechnical University Press, p.175–187, 1998. (in Chinese).

Google Scholar

[3] Julier S and Uhlmann J. K., A general method for approximating nonlinear transformations of probability distributions [R], Robotics Research Group, Department of Engineering Science, University of Oxford, (1994).

Google Scholar

[4] Wang Danli and Zhang Hongyue, Nonlinear filtering algorithm for INS initial alignment, Journal of Chinese Inertial Technology, vol. 7, p.17–21, Sep. 1999, article ID: 1005–6734 (1999) 03–0017–05. (in chinese).

Google Scholar

[5] Ding Yangbin and Shen Gongxun, Study on unscented particle filter applied in initial alignment of large azimuth misalignment on static base of SINS, Chinese Journal of Aeronautics, vol. 28, No. 2, p.397–398, Mar. 2007, article ID: 1000–6893 (2007).

Google Scholar

[6] Julier S J. The spherical simplex unscented transformation [C], American Control Conference (IEEE), vol. 3, p.2430–2434, Denver, Colorado, (2003).

Google Scholar

[7] Andersen M N, and Wheller K. Filtering in hybrid dynamic bayesian networks [C], IEEE International Conference on Acoustics, Speech, and Signal Processing, p.773–776, (2004).

DOI: 10.1109/icassp.2004.1327225

Google Scholar

[8] Rudolph van der Merwe and Eric A. Wan. The square-root unscented Kalman filter for state and parameter estimation, IEEE Communication and Control, p.3461–3464, (2000).

DOI: 10.1109/icassp.2001.940586

Google Scholar