A fast quasi-Monte Carlo-based particle filter algorithm

被引：1

作者：

Zhao L.-L. ^{[1
]}

Ma P.-J. ^{[1
]}

Su X.-H. ^{[1
]}

机构：

[1] School of Computer Science and Technology, Harbin Institute of Technology

来源：

Zidonghua Xuebao/Acta Automatica Sinica | 2010年 / 36卷 / 09期

关键词：

Particle filters; Quasi-Monte Carlo (QMC); Resampling algorithm; Sample impoverishment;

D O I：

10.3724/SP.J.1004.2010.01351

中图分类号：

学科分类号：

摘要：

Particle filters have a high computational complexity when using quasi-Monte Carlo (QMC) methods, and are subject to the sample impoverishment caused by resampling step. To solve these problems, a new particle filter algorithm based on QMC method is proposed. It generates the randomized QMC points after sample importance, and then transforms them into some independent sub-spaces, whose kernels are the particles with heavy weights, to avoid predicting the sampling space and preserve the diversity of samples. The simulation results suggest that the algorithm can escape successfully from the sample impoverishment, provide more accurate estimators than the Monte Carlo (MC) method, meanwhile has a computational cost similar to the general particle filter. Copyright © 2010 Acta Automatica Sinica. All rights reserved.

引用

页码：1351 / 1356

页数：5

共 12 条

[1]

Arulampalam M.S., Maskell S., Gordon N., Clapp T., A tutorial on particle filters for on-line nonlinear/non-Gaussian Bayesian tracking, IEEE Transactions on Signal Processing, 50, 2, pp. 174-188, (2002)

[2]

Gordon N., Salmond D.J., Smith A.F.M., Novel approach to nonlinear/non-Gaussian Bayesian state estimation, IEEE Proceedings F: Radar and Signal Processing, 140, 2, pp. 107-113, (1993)

[3]

Khan Z., Balch T., Dellaert F., MCMC-based particle filtering for tracking a variable number of interacting targets, IEEE Transactions on Pattern Analysis and Machine Intelligence, 27, 11, pp. 1805-1819, (2005)

[4]

Musso C., Oudjane N., LeGland F., Improving regularised particle filters, Sequential Monte Carlo Methods in Practice, pp. 247-271, (2001)

[5]

L'Ecuyer P., Quasi-Monte Carlo methods with applications in finance, Finance and Stochastics, 13, 3, pp. 307-349, (2009)

[6]

Boyle P.P., Tan K.S., Quasi-Monte Carlo methods, Proceedings of the 7th International AFIR Colloquium, pp. 1-24, (1997)

[7]

Joe S., Kuo F.Y., Constructing Sobol' sequences with better two-dimensional projections, SIAM Journal on Scientific Computing, 30, 5, pp. 2635-2654, (2008)

[8]

Perez C.J., Martin J., Rufo M.J., Rojano C., Quasi-random sampling importance resampling, Communications in Statistics: Simulation and Computation, 34, 1, pp. 97-112, (2005)

[9]

Guo D., Wang X.D., Quasi-Monte Carlo filtering in nonlinear dynamic systems, IEEE Transactions on Signal Processing, 54, 6, pp. 2087-2098, (2006)

[10]

Fearnhead P., Using random quasi-Monte-Carlo within particle filters, with application to financial time series, Journal of Computational and Graphical Statistics, 14, 4, pp. 751-769, (2005)

← 1 2 →