A Novel Adaptive UKF and its Application in the SINS/GPS Integrated Navigation

Yong Li; She Sheng Gao; Yi Yang

doi:10.4028/www.scientific.net/AMM.597.521

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HomeApplied Mechanics and MaterialsApplied Mechanics and Materials Vol. 597A Novel Adaptive UKF and its Application in the...

A Novel Adaptive UKF and its Application in the SINS/GPS Integrated Navigation

Abstract:

This paper reports the solution of the state estimation problem of nonlinear systems without knowing prior noise statistical characteristics. An adaptive UKF algorithm is proposed. This novel UKF algorithm is constructed by traditional UKF and EM algorithm, also it improve accuracy through Taylor series approximation. By applying the proposed algorithm into SINS/GPS integrated navigation system and comparing with the unscented Kalman filtering (UKF) algorithm, the adaptive UKF algorithm we proposed can effectively improve the filtering performance , also it outperforms UKF in terms of accuracy.

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Periodical:

Applied Mechanics and Materials (Volume 597)

Pages:

521-524

DOI:

https://doi.org/10.4028/www.scientific.net/AMM.597.521

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Online since:

July 2014

Authors:

Yong Li*, She Sheng Gao, Yi Yang

Keywords:

EM Algorithm, Integrated Navigation, SINS/GPS, UKF

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* - Corresponding Author

References

[1] Zhao Lin, Wang Xiao-Xu, Sun Ming, Ding Ji-Cheng, YanChao. Adaptive UKF filtering algorithm based on maximum a posterior estimation and exponential weighting. Acta Au-tomatica Sinica, 2010, 36(7): 1007-1019.

DOI: 10.3724/sp.j.1004.2010.01007

Google Scholar

[2] J. Valappil, C. Georgakis, Systematic estimation of state noise statistics for extended Kalman filters, AIChE J. 46 (2) (2000) 292-308.

DOI: 10.1002/aic.690460209

Google Scholar

[3] Den¾ux T. Maximum likelihood estimation from fuzzy data using the EM algorithm. Fuzzy Sets and Systems, 2011, 183(1): 72-91.

DOI: 10.1016/j.fss.2011.05.022

Google Scholar

[4] Xue Li, Gao shesheng, Zhao Yan. Unscented particle filtering with adaptive adjusted weight and its application in integrated navigation[J]. Journal of Chinese Inertial Technology. 2012, 20(4): 459-463.

Google Scholar

[5] G.C. Goodwin, J.C. Aguero, Approximate EM algorithms for parameter and state estimation in nonlinear stochastic models, 44th IEEE Conference on Decision and Control, 2005, p.368–373.

DOI: 10.1109/cdc.2005.1582183

Google Scholar

[6] M.S. Arulampalam, S. Maskell, T. Clapp, A tutorial on particle filters for online nonlinear/non-Gaussian Bayesian tracking, IEEE Trans. Signal Process. 50 (2) (2002) 174–188.

DOI: 10.1109/78.978374

Google Scholar

[7] D.M. Himmelblau, Process Analysis by Statistical Methods, John Wiley & Sons Inc., New York, (1970).

Google Scholar

[8] Bavdekar V A, Deshpande A P, Patwardhan S C. Identification of process and measurement noise covariance for state and parameter estimation using extended Kalman filter. Journal of Process Control, 2011, 21(4): 585-601.

DOI: 10.1016/j.jprocont.2011.01.001

Google Scholar