Semi-Independent Resampling for Particle Filtering

被引：5

作者：

Lamberti, Roland ^{[1
]}

Petetin, Yohan ^{[1
]}

Desbouvries, Francois ^{[1
]}

Septier, Francois ^{[2
]}

机构：

[1] Univ Paris Saclay, CNRS, Telecom Sudparis, F-91011 Evry, France

[2] Univ Lille, CNRS, CRIStAL, UMR 9189,IMT Lille Douai, F-59000 Lille, France

来源：

IEEE SIGNAL PROCESSING LETTERS | 2018年 / 25卷 / 01期

关键词：

Particle filters; resampling; sequential Monte Carlo algorithms;

D O I：

10.1109/LSP.2017.2775150

中图分类号：

TM [电工技术]; TN [电子技术、通信技术];

学科分类号：

0808 ; 0809 ;

摘要：

Among sequential Monte Carlo methods, sampling importance resampling (SIR) algorithms are based on importance sampling and on some (resampling-based) rejuvenation algorithm that aims at fighting against weight degeneracy. However, this mechanism tends to be insufficient when applied to informative or high-dimensional models. In this letter, we revisit the rejuvenation mechanism and propose a class of parameterized SIR-based solutions that enable us to adjust the tradeoff between computational cost and statistical performances.

引用

页码：130 / 134

页数：5

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