Research on Adaptive Unscented Kalman Filter for Integrated Navigation

Tian Lai Xu

doi:10.4028/www.scientific.net/AMR.753-755.2582

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HomeAdvanced Materials ResearchAdvanced Materials Research Vols. 753-755Research on Adaptive Unscented Kalman Filter for...

Research on Adaptive Unscented Kalman Filter for Integrated Navigation

Abstract:

The accuracy of filtering deteriorates in condition that a priori information used in unscented Kalman filter (UKF) does not accord with the actual conditions. To improve the accuracy of filtering when the noise statistical properties are not known exactly in navigational data fusion, an adaptive UKF is proposed. In the filtering process, the statistical parameters of unknown system noises are adjusted online if filtering abnormality exists. Simulation results show that the proposed algorithm increases the accuracy compared with the standard UKF algorithm for integrated navigation.

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Periodical:

Advanced Materials Research (Volumes 753-755)

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2582-2585

DOI:

https://doi.org/10.4028/www.scientific.net/AMR.753-755.2582

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Online since:

August 2013

Authors:

Tian Lai Xu

Keywords:

Adaptive Filter, Data Fusion, INS/GPS, Unscented Kalman Filter

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References

[1] F.A. Farhan, K.J. Turner, Extended Kalman filter synthesis for integrated global positioning/ inertial navigation systems, Applied Mathematics and Computation, vol. 115(2000), pp.213-227.

DOI: 10.1016/s0096-3003(98)10068-1

Google Scholar

[2] S. Julier, J.K. Uhlmann, Unscented filtering and nonlinear estimation. Proceedings of the IEEE, (2004), pp.401-420.

Google Scholar

[3] S. Julier, J.K. Uhlmann, et al. A new method for the nonlinear transformation of means and covariance in filters and estimators. IEEE Trans, A. C., vol. 45(2000), pp.477-482.

DOI: 10.1109/9.847726

Google Scholar

[4] C. Hide, T. Moore ,M. Smith, Adaptive Kalman filtering for low-cost INS/GPS, The Journal of Navigation, vol. 56(2003), pp.143-152.

DOI: 10.1017/s0373463302002151

Google Scholar

[5] L. Wang, G.C. Li, X.W. Qiao, et al, An adaptive UKF algorithm based on maximum likelihood principle and expectation maximization algorithm, ACTA AUTOMATICA SINICA, vol. 38 (2012), pp.1200-1210.

DOI: 10.3724/sp.j.1004.2012.01200

Google Scholar

[6] T. Kirubarajan ,Y. Bar-Shalom, Kalman filter versus IMM estimator: when do we need the latter? IEEE TRANS on Aerospace and Electronic Systems, vol. 39(2003) pp.1452-1457.

DOI: 10.1109/taes.2003.1261143

Google Scholar