Abstract
In order to solve the flexure compensation problem in rapid transfer alignment, the error equations are simplified by noise compensation method firstly. Due to the time variant characteristics of flexure process in time domain, which leads to the fixed noise statistical characteristics cannot follow the variation of actual environment, the noise estimator with limited memory index is proposed. By limiting the memory length of obtained data, too old historical data is giving up and the accuracy of online noise estimator is improved. The final simulation verifies that the method proposed have higher accuracy and faster convergence speed than conventional methods.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Bavdekar VA, Deshpande AP, Patwardhan SC (2011) Identification of process and measurement noise covariance for state and parameter estimation using extended Kalman filter. J Process Control 21(4):585–601
Jones D, Roberts C, Tarrant D (1993) Transfer alignment design and evaluation environment. In: IEEE proceedings of aerospace control systems, pp 753–757
Kain JE, Cloutier JR (1989) Rapid transfer alignment for tactical weapon application. In: Proceedings of the AIAA guidance, navigation and control conference, Boston, pp 1290–1300
Lim Y-C, Lyou J (2001) An error compensation method for transfer alignment. In: Proceedings of IEEE conference on electrical and electronic technology. TENCON, vol 2, pp 850–855
Mohamed AH (1999) Adaptive Kalman filtering for INS/GPS. J Geodesy 73(4):193–203
Qi S, Han J-D (2008) An adaptive UKF algorithm for the state and parameter estimation of a mobile robot. Acta Automatica Sinica 34(1):72–79
Robert MR (1996) Weapon IMU transfer alignment using aircraft position from actual flight tests. In: Proceedings of IEEE position location and navigation symposium, pp 328–335
Ross CC, Elbert TF (1994) A transfer alignment algorithm study based on actual flight test data from a tactical air-to-ground weapon launch. In: Proceedings of IEEE position location and navigation symposium, pp 431–438
Sage AP, Husa GW (1969) Adaptive filtering with unknown prior statistics. In: Joint automatic control conference, Colombia, pp 760–769
Spalding K (1992) An efficient rapid transfer alignment filter. In: Proceedings of the AIAA guidance, navigation and control conference, pp 1276–1286
Wendel J, Metzger J., Trommer GF (2004) Rapid transfer alignment in the presence of time correlated measurement and system noise. In: AIAA guidance, navigation, and control conference and exhibit, Providence, RI, pp 1–12
Xiao Y, Zhang H (2001) Study on transfer alignment with the wing flexure of aircraft. Aerosp Control 2:27–35
Xiong K, Zhang HY, Chan CW (2006) Performance evaluation of UKF-based nonlinear filtering. Automatica 42(2):261–270
Xiong K, Zhang HY, Chan CW (2007) Author’s reply to “comments on ‘performance evaluation of UKF-based nonlinear filtering’”. Automatica 43(3):569–570
Zhao L, Wang X (2009) Design of unscented Kalman filter with noise statistic estimator. Control Decis 24(10):1483–1488
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2013 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Zhou, Wd., Ji, Yr. (2013). Application of Noise Estimator with Limited Memory Index on Flexure Compensation of Rapid Transfer Alignment. In: Qi, E., Shen, J., Dou, R. (eds) The 19th International Conference on Industrial Engineering and Engineering Management. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-38391-5_153
Download citation
DOI: https://doi.org/10.1007/978-3-642-38391-5_153
Published:
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-38390-8
Online ISBN: 978-3-642-38391-5
eBook Packages: Business and EconomicsBusiness and Management (R0)