Square Root Cubature Particle Filter

Jing Mu; Yuan Li Cai; Jun Min Zhang

doi:10.4028/www.scientific.net/AMR.219-220.727

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HomeAdvanced Materials ResearchAdvanced Materials Research Vols. 219-220Square Root Cubature Particle Filter

Square Root Cubature Particle Filter

Abstract:

The square root cubature particle filter (SRCPF) uses the square root cubature Kalman filter (SRCKF) for generating the proposal distribution. The SRCPF algorithm is easy to be implemented and has numerical stability. Moreover, the SRCKF based proposal distribution approximates the optimal importance distribution by incorporating the current measurement. Simulation results demonstrate that the SRCPF algorithm has the better performance for state estimation than the generic particle filter (GPF), extended particle filter (EPF) and unscented particle filter (UPF), and its calculation cost decreases largely.

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Periodical:

Advanced Materials Research (Volumes 219-220)

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727-731

DOI:

https://doi.org/10.4028/www.scientific.net/AMR.219-220.727

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Online since:

March 2011

Authors:

Jing Mu, Yuan Li Cai, Jun Min Zhang

Keywords:

Cubature Kalman Filter, Particle Filter (PF), Proposal Distribution

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References

[1] A.Doucet, J.F.G.De Freitas, and N.J. Gordon, An introduction to sequential Monte Carlo methods, in: Sequential Monte Carlo methods in practice, Doucet A, Gordon N. New York: Springer-Verlag (2001).

DOI: 10.1007/978-1-4757-3437-9_1

Google Scholar

[2] M.S. Arulampalam, S.Askell, N.Gordon, et al. A tutorial on particle filters for online nonlinear/non-Gaussian Bayesian tracking. IEEE Trans on Signal Processing, Vol.50 No.2 (2002), pp.174-188.

DOI: 10.1109/78.978374

Google Scholar

[3] N.J. Gordon, D.J. Salmond, A.F.M. Smith, Novel approach to nonlinear/non-Gaussian Bayesian state estimation, IEE Proceedings-F. Vol.140 No.2 (1993), pp.107-113.

DOI: 10.1049/ip-f-2.1993.0015

Google Scholar

[4] A. Doucet. On sequential Monte Carlo sampling methods for Bayesian filtering [R]. Cambridge: University of Cambridge, (1998), pp.1-36.

Google Scholar

[5] M.K. Pitt, N.Shephard. Filtering via simulation: Auxiliary particle filters. Journal of the American Statistical Association, Vol. 94 No. 2 (1999), pp.590-599.

DOI: 10.1080/01621459.1999.10474153

Google Scholar

[6] C.Musso, N.Oudjane, and F.Legland, Improving regularized particle filters, in Sequential Monte Carlo methods in practice, Doucet A, Gordon N. New York: Springer-Verlag( 2001).

DOI: 10.1007/978-1-4757-3437-9_12

Google Scholar

[7] R.VandermerweT, A.Doucet, TJ.F.G.De FreitasT, et al. The unscented particle filter, CUED/F-IN- FEN G/TR, 380 . Cambridge, UK: Cambridge University Press, (2000), pp.1-45. T.

Google Scholar

[8] T TI.Arasaratnam, S.Haykin. Cubature Kalman Filters. IEEE Trans on automatic control. Vol. 54 No. 6, (2009), pp.1254-1269.T.

DOI: 10.1109/tac.2009.2019800

Google Scholar