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
收藏
页码：130 / 134
页数：5
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