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On-line Access: 2015-11-04

Received: 2015-06-24

Revision Accepted: 2015-09-01

Crosschecked: 2015-09-10

Cited: 8

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Citations:  Bibtex RefMan EndNote GB/T7714


Tian-cheng Li


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Frontiers of Information Technology & Electronic Engineering  2015 Vol.16 No.11 P.969-984


Resampling methods for particle filtering: identical distribution, a new method, and comparable study

Author(s):  Tian-cheng Li, Gabriel Villarrubia, Shu-dong Sun, Juan M. Corchado, Javier Bajo

Affiliation(s):  1BISITE Group, Faculty of Science, University of Salamanca, C/Espejo s/n, Salamanca 37008, Spain; more

Corresponding email(s):   t.c.li@usal.es, t.c.li@mail.nwpu.edu.cn

Key Words:  Particle filter, Resampling, Kullback-Leibler divergence, Kolmogorov-Smirnov statistic

Tian-cheng Li, Gabriel Villarrubia, Shu-dong Sun, Juan M. Corchado, Javier Bajo. Resampling methods for particle filtering: identical distribution, a new method, and comparable study[J]. Frontiers of Information Technology & Electronic Engineering, 2015, 16(11): 969-984.

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publisher="Zhejiang University Press & Springer",

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%T Resampling methods for particle filtering: identical distribution, a new method, and comparable study
%A Tian-cheng Li
%A Gabriel Villarrubia
%A Shu-dong Sun
%A Juan M. Corchado
%A Javier Bajo
%J Frontiers of Information Technology & Electronic Engineering
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%D 2015
%I Zhejiang University Press & Springer
%DOI 10.1631/FITEE.1500199

T1 - Resampling methods for particle filtering: identical distribution, a new method, and comparable study
A1 - Tian-cheng Li
A1 - Gabriel Villarrubia
A1 - Shu-dong Sun
A1 - Juan M. Corchado
A1 - Javier Bajo
J0 - Frontiers of Information Technology & Electronic Engineering
VL - 16
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EP - 984
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DOI - 10.1631/FITEE.1500199

resampling is a critical procedure that is of both theoretical and practical significance for efficient implementation of the particle filter. To gain an insight of the resampling process and the filter, this paper contributes in three further respects as a sequel to the tutorial (Li et al., 2015). First, identical distribution (ID) is established as a general principle for the resampling design, which requires the distribution of particles before and after resampling to be statistically identical. Three consistent metrics including the (symmetrical) kullback-Leibler divergence, kolmogorov-Smirnov statistic, and the sampling variance are introduced for assessment of the ID attribute of resampling, and a corresponding, qualitative ID analysis of representative resampling methods is given. Second, a novel resampling scheme that obtains the optimal ID attribute in the sense of minimum sampling variance is proposed. Third, more than a dozen typical resampling methods are compared via simulations in terms of sample size variation, sampling variance, computing speed, and estimation accuracy. These form a more comprehensive understanding of the algorithm, providing solid guidelines for either selection of existing resampling methods or new implementations.

This paper provides a further extension to the tutorial Li et al., 2015. First, an identical distribution (ID) principle for re-sampling design is proposed, together with three metrics to evaluate the ID attribute. Next, a new re-sampling method which can achieve the optimal sampling variance is presented. Last, the effectiveness of the proposed method is verified by comparison with conventional re-sampling methods. This paper is well-written and provides great insight into the re-sampling procedure of the particle filter. It will be a good guidance for the researchers working on particle filter.


创新点:理论上严格定义了同分布原则作为重采样方法设计的普遍性原则,给出三种同分布测度方法;提出了一种最小采样方差(MSV: minimum sampling variance)最优重采样方法,在满足渐近无偏性的前提下获得最小采样方差。
方法:给出三种“重采样同分布”测度方法:Kullback-Leibler偏差,Kolmogorov-Smirnov统计和采样方差(sampling variance)。所提出的最小采样方差重采样放宽了无偏性条件,仅满足渐近无偏,但获得了最小采样方差(参见定理2-4论证以及仿真性能对比)。


Darkslateblue:Affiliate; Royal Blue:Author; Turquoise:Article


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