A Novel Modified Particle Filter Algorithm

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Abstract:

The particle filter (PF) algorithm provides an effective solution to the non-linear and non-Gaussian filtering problem. However, when the motion noises or observation noises are strong, the degenerate phenomena will occur, which leads to poor estimation. In this paper, we propose a modified particle filter (MPF) algorithm for improving the estimated precision through a particle optimization method. After calculating the coarse estimation with the traditional PF, we optimize the particles according to their weights and relative positions, then, move the particles toward the optimal probability distribution. The state estimation and target tracking experiments demonstrate the outstanding performance of the proposed algorithm.

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Periodical:

Advanced Materials Research (Volumes 204-210)

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1895-1899

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February 2011

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© 2011 Trans Tech Publications Ltd. All Rights Reserved

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[1] A. Doucet, S. Godsill and C. Andrieu: On Sequential Monte Carlo Sampling Methods for Bayesian Filtering. Statistics and Computing, 2000. 10(3): pp.197-208.

DOI: 10.1023/a:1008935410038

Google Scholar

[2] R. Merwe, A. Doucet, N. Freitas and E. Wan: The Unscented Particle Filter, The 14th Annual Neural Information Processing Systems Conference, Denver, Colorada, USA, 2000: pp.584-590.

Google Scholar

[3] Q. Cheng and P. Bondon: A New Unscented Particle Filter, International Conference on Acoustics. Speech and Signal Processing, Las Vegas, Nevada, USA, 2008: pp.3417-3420.

DOI: 10.1109/icassp.2008.4518385

Google Scholar

[4] A. M. Johansen and A. Doucet: A Note on Auxiliary Particle Filters. Statistics and Probability Letters, 2008. 78(12): pp.1498-1504.

DOI: 10.1016/j.spl.2008.01.032

Google Scholar

[5] J. H. Kotecha and P. M. Djuric: Gaussian Sum Particle Filtering. IEEE Transactions on Signal Processing, 2003. 51(10): pp.2602-2612.

DOI: 10.1109/tsp.2003.816754

Google Scholar

[6] P. M. Djuric, Z. Zhang and M. F. Bugallo: Target Tracking by a New Class of Cost-reference Particle Filters. IEEE Aerospace Conference, Big Sky, Montana, 2008: pp.1-9.

DOI: 10.1109/aero.2008.4526444

Google Scholar

[7] D. Givon, P. Stinis and J. Weare: Variance Reduction for Particle Filters of Systems with Time Scale Separation. IEEE Transactions on Signal Processing, 2009. 57(2): pp.424-435.

DOI: 10.1109/tsp.2008.2008252

Google Scholar