The Research on Target State Estimation Based on Multi-scale Particle Filtering Algorithm

ZHAI  Yongzhi

doi:10.15892/j.cnki.djzdxb.2011.04.034

Journal of Projectiles, Rockets, Missiles and Guidance >

2011 , Vol. 31 >Issue 4: 214 - 217

DOI: https://doi.org/10.15892/j.cnki.djzdxb.2011.04.034

CORRELATION TECHNOLOGY

The Research on Target State Estimation Based on Multi-scale Particle Filtering Algorithm

ZHAI Yongzhi

Expand

School of Electronics and Information, Northwestern Polytechnical University, Xi'an 710072, China

Received date: 2009-09-22

Online published: 2025-05-30

Fold

Abstract

Based on the given observation data, the multi-scale filtering algorithm was proposed for extracting optimum value with respect to the target state by using different fine and coarse scales in a single chain to explore a maximum posterior likelihood distribution function of the state information. Hence, the state information concerning maneuvering target optimum estimation can be achieved via the novel algorithm based on both Gibbs and Metropolis-Hasting important sampling. The fine scales guaranteed the precision of estimation while the coarse scales enhanced computational efficiency. The simulation shows the fact that the good tradeoff between the estimation accuracy and computational efficiency.

Key words： multi-scale particle filtering; target state information; maximum posterior distribution function; important sampling; Markov chain

Cite this article

ZHAI Yongzhi . The Research on Target State Estimation Based on Multi-scale Particle Filtering Algorithm[J]. Journal of Projectiles, Rockets, Missiles and Guidance, 2011 , 31(4) : 214 -217 . DOI: 10.15892/j.cnki.djzdxb.2011.04.034

[an error occurred while processing this directive]

References

[1]	MK Pitt, N Shephard. Filtering via simulation: Auxiliary particle filters[J]. Jourmal of the American Statistical Association, 1999, 94(446): 590-599.
[2]	A Doucet. On sequence simulation-based method for Bayesian filtering [R]. Dept. Eng., Univ. Cambrige. U. K.: Tech. Rep. CUED/F-IFENG/IR. 310, 1998.
[3]	C Adrieu, A Doucet, S Godsill. On sequential Monte Carlo sampling methods for Bayesian filtering [R]. Dept. Eng., Univ. Cambrige. U. K.: Tech. Rep. U. K., 1999.
[4]	A Doucet, N J Gordon, Krishnamuethy. Particle filter for state estimation of jump Markov linear systems [R]. Tech. Rep. Defence Eval. Res. Agency., 1999.
[5]	J S liu, Chen Yong. Sequence Monte Carlo method for dynamic systems [R]. Tech Dept. Stat. Univ. Stanford. CA., 1998.
[6]	F J FLORIS, M D Bush, M Cuypers, et al. Comparison of production forecast uncertainty quantification methods an integrated study [C]// Proc. 1st Symp. Petroleum Geostatic. Toulouse. France. Apr. 20-23, 1999.
[7]	J W Barker, M Cuypers, L Holdden. Quantifying uncertainty in production forecasts: Another look at the PUNQ-S3 problem [C]// Proc, Soc. Petroleum Eng. annu. Tech. Conf., 2000.
[8]	Dave Higdon, Herbert Lee, Zhouxin Bi. A Bayesian approach to charactering uncertainty in inverse problems using coarse and fine-scale information[J]. IEEE Trans. Signal Process, 2007, 50(2): 389-398.
[9]	Matthew Orton, William Fitzgerald. A Bayesian approach to tracking multiple targets using sensor arrays and particle filters [J]. IEEE Trans. Signal Process., 2002, 50(2): 216-223.

Options

Outlines