Resampling Methods for Particle Filtering

被引：477

作者：

Li, Tiancheng ^{[1
,2
]}

Bolic, Miodrag ^{[3
,4
,5
,6
]}

Djuric, Petar M. ^{[7
]}

机构：

[1] Northwestern Polytech Univ, Mechatron Engn, Xian, Peoples R China

[2] Univ Salamanca, Bioinformat Intelligent Syst & Educ Technol Grp, E-37008 Salamanca, Spain

[3] Univ Ottawa, Sch Elect Engn & Comp Sci, Ottawa, ON K1N 6N5, Canada

[4] Univ Ottawa, Comp Architecture Res Grp, Ottawa, ON K1N 6N5, Canada

[5] Univ Ottawa, Radio Frequency Identificat Res Grp, Ottawa, ON K1N 6N5, Canada

[6] Univ Ottawa, Med Devices Res Grp, Ottawa, ON K1N 6N5, Canada

[7] SUNY Stony Brook, Dept Elect & Comp Engn, Stony Brook, NY 11794 USA

来源：

IEEE SIGNAL PROCESSING MAGAZINE | 2015年 / 32卷 / 03期

基金：

中国国家自然科学基金;

关键词：

SAMPLE-SIZE; ALGORITHMS; INFERENCE; SYSTEMS;

D O I：

10.1109/MSP.2014.2330626

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：

Two decades ago, with the publication of [1], we witnessed the rebirth of particle filtering (PF) as a methodology for sequential signal processing. Since then, PF has become very popular because of its ability to process observations represented by nonlinear state-space models where the noises of the model can be non-Gaussian. This methodology has been adopted in various fields, including finance, geophysical systems, wireless communications, control, navigation and tracking, and robotics [2]. The popularity of PF has also spurred the publication of several review articles [2]-[6]. © 1991-2012 IEEE.

引用

页码：70 / 86

页数：17

共 50 条

[1]

[Anonymous], 2009, Handbook of Nonlinear Filtering

[2] A tutorial on particle filters for online nonlinear/non-Gaussian Bayesian tracking [J].