Particle Filtering Optimized by Swarm Intelligence Algorithm

Wei Jing, Hai Zhao, Chunhe Song, Dan Liu
DOI: 10.4236/jilsa.2010.21007                

Abstract

A new filtering algorithm — PSO-UPF was proposed for nonlinear dynamic systems. Basing on the concept of re-sampling, particles with bigger weights should be re-sampled more time, and in the PSO-UPF, after calculating the weight of particles, some particles will join in the refining process, which means that these particles will move to the region with higher weights. This process can be regarded as one-step predefined PSO process, so the proposed algo-rithm is named PSO-UPF. Although the PSO process increases the computing load of PSO-UPF, but the refined weights may make the proposed distribution more closed to the poster distribution. The proposed PSO-UPF algorithm was compared with other several filtering algorithms and the simulating results show that means and variances of PSO-UPF are lower than other filtering algorithms.

Keywords

Filtering Method, Particle Filtering, Unscented Kalman Filter, Particle Swarm Optimizer

Conflicts of Interest

The authors declare no conflicts of interest.

References

[1]	D. Guo and X. Wang, “Quasi-monte Carlo filtering in nonlinear dynamic systems,” IEEE Transactions on Signal Process, Vol. 54, No.6, pp. 2087–2098, 2006.
[2]	M. S. Arulampalam, S. Maskell, N. Gordon, et al., “A tutorial on particle filters for online nonlinear/non-gau- ssian bayesian tracking [J],” IEEE Transactions on Signal Processing, Vol. 20, No. 2, pp. 174–188, 2002.
[3]	D. Crisan and A. Doucet, “A survey of convergence results on particle filtering methods for practitioners [J],” IEEE Transactions on Signal Processing, Vol. 50, No. 3, pp. 736–746, 2002.
[4]	B. D. Anderson, and J. B. Moore, “Optimal filtering,” Prentice–Hall, New Jersey, 1979.
[5]	S. J. Julier and J. K. Uhlmann, “A new extension of the Kalman filter to nonlinear systems,” Proceedings of AeroSense: The 11th International Sysmpsium on Aerospace/Defence Sensing, Simulation and Controls, Orlando, Florida, Muti Sensor Fusion, Tracking and Resource Management II, pp. 182–193. 1997.
[6]	Y. Shi, and R. C. Eberhart, “A modified particle swarm optimizer,” in Proceedings of the IEEE International Conference on Evolutionary Computation, Piscataway, NJ: IEEE Press, pp. 69–73, 1998.
[7]	J. A Riget, “Diversity-guided particle swarm optimizer —the ARPSO,” EVALife Technical Report, Department of Computer Science, University of Arhus, 2002.
[8]	D. Guo, X. Wang, and R. Chen, “New sequential monte carlo methods for nonlinear dynamic systems,” Statistics and Computing, Vol. 15, No. 2, pp. 135–147, 2005.
[9]	Y. Bar-Shalom, X. R. Li, and T. Kirubarajan, “Estimation with applications to tracking and navigation: Theory, Algorithm and Software [M],” New York: Wiley, 2001.