Novel Initialization Method for Nonlinear Kalman Filters

Yu Zhe Wang; Hang Su; Yu Cao; Han Liu

doi:10.4028/www.scientific.net/AMR.834-836.1047

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HomeAdvanced Materials ResearchAdvanced Materials Research Vols. 834-836Novel Initialization Method for Nonlinear Kalman...

Novel Initialization Method for Nonlinear Kalman Filters

Abstract:

A novel initialization method for nonlinear Kalman filter was proposed aimed at the defects of the ordinary initialization method. This method was stricter than the ordinary method in theory and could guarantee scientific compare between nonlinear Kalman filters based on Gaussian assumptions. Simulation results showed that the novel method greatly reduced the impact of initialization and reflected more the estimation error due to the type of nonlinear Kalman filters rather than initialization.

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Periodical:

Advanced Materials Research (Volumes 834-836)

Pages:

1047-1050

DOI:

https://doi.org/10.4028/www.scientific.net/AMR.834-836.1047

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Online since:

October 2013

Authors:

Yu Zhe Wang*, Hang Su, Yu Cao, Han Liu

Keywords:

Gaussian Assumption, Initialization, Kalman Filter (KF), Nonlinear Filter

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References

[1] Y. Bar-Shalom, Xiaorong Li, and T. Kirubarajan: Estimation with Application to Tracking and Navigation: Theory, Algorithm, and Software. (John Wiley & Sons, Inc., USA 2001).

Google Scholar

[2] Carlo Novara, Fredy Ruiz, Mario Milanese. Direct Filtering: A New Approach to Optimal Filter Design for Nonlinear Systems. IEEE Transactions on Automatic Control, Vol. 58 (2013), pp.86-99.

DOI: 10.1109/tac.2012.2204160

Google Scholar

[3] X. R. Li, and Y. M. Zhang. Numerically Robust Implementation of Multiple-Model Algorithms. IEEE Transaction on Aerospace and Electronic Systems, Vol. 36 (2000), pp.266-278.

DOI: 10.1109/7.826329

Google Scholar

[4] Jing Mu, Yuan Li Cai, Jun Min Zhang. Square Root Cubature Particle Filter. Advanced Materials Research, Vol. 219 (2011), pp.727-731.

DOI: 10.4028/www.scientific.net/amr.219-220.727

Google Scholar

[5] Lubin Chang, Baiqin Hu, An Li : Transformed Unscented Kalman Filter. IEEE Transactions on Automatic Control, Vol. 58 (2013), pp.252-257.

DOI: 10.1109/tac.2012.2204830

Google Scholar