Sequential Monte Carlo: A Unified Review

被引：17

作者：

Wills, Adrian G. ^{[1
]}

Schon, Thomas B. ^{[2
]}

机构：

[1] Univ Newcastle, Sch Engn, Callaghan, NSW, Australia

[2] Uppsala Univ, Dept Informat Technol, Uppsala, Sweden

来源：

ANNUAL REVIEW OF CONTROL ROBOTICS AND AUTONOMOUS SYSTEMS | 2023年 / 6卷

基金：

瑞典研究理事会;

关键词：

sequential Monte Carlo; particle filter; nonlinear state-space model; state estimation; system identification; PARAMETER-ESTIMATION; MAXIMUM-LIKELIHOOD; SIMULATION METHODS; SAMPLING METHODS; GIBBS;

D O I：

10.1146/annurev-control-042920-015119

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

Sequential Monte Carlo methods-also known as particle filters-offer approximate solutions to filtering problems for nonlinear state-space systems. These filtering problems are notoriously difficult to solve in general due to a lack of closed-form expressions and challenging expectation integrals. The essential idea behind particle filters is to employ Monte Carlo integration techniques in order to ameliorate both of these challenges. This article presents an intuitive introduction to the main particle filter ideas and then unifies three commonly employed particle filtering algorithms. This unified approach relies on a nonstandard presentation of the particle filter, which has the advantage of highlighting precisely where the differences between these algorithms stem from. Some relevant extensions and successful application domains of the particle filter are also presented.

引用

页码：159 / 182

页数：24

共 66 条

[1] Observation of Gravitational Waves from a Binary Black Hole Merger [J].

Abbott, B. P. ;

Abbott, R. ;

Abbott, T. D. ;

Abernathy, M. R. ;

Acernese, F. ;

Ackley, K. ;

Adams, C. ;

Adams, T. ;

Addesso, P. ;

Adhikari, R. X. ;

Adya, V. B. ;

Affeldt, C. ;

Agathos, M. ;

Agatsuma, K. ;

Aggarwal, N. ;

Aguiar, O. D. ;

Aiello, L. ;

Ain, A. ;

Ajith, P. ;

Allen, B. ;

Allocca, A. ;

Altin, P. A. ;

Anderson, S. B. ;

Anderson, W. G. ;

Arai, K. ;

Arain, M. A. ;

Araya, M. C. ;

Arceneaux, C. C. ;

Areeda, J. S. ;

Arnaud, N. ;

Arun, K. G. ;

Ascenzi, S. ;

Ashton, G. ;

Ast, M. ;

Aston, S. M. ;

Astone, P. ;

Aufmuth, P. ;

Aulbert, C. ;

Babak, S. ;

Bacon, P. ;

Bader, M. K. M. ;

Baker, P. T. ;

Baldaccini, F. ;

Ballardin, G. ;

Ballmer, S. W. ;

Barayoga, J. C. ;

Barclay, S. E. ;

Barish, B. C. ;

Barker, D. ;

Barone, F. .

PHYSICAL REVIEW LETTERS, 2016, 116 (06)

[2]

Andersson C, 2019, IEEE DECIS CONTR P, P3670, DOI [10.1109/cdc40024.2019.9030219, 10.1109/CDC40024.2019.9030219]

[3] Particle Markov chain Monte Carlo methods [J].

Andrieu, Christophe ;

Doucet, Arnaud ;

Holenstein, Roman .

JOURNAL OF THE ROYAL STATISTICAL SOCIETY SERIES B-STATISTICAL METHODOLOGY, 2010, 72 :269-342

[4] THE PSEUDO-MARGINAL APPROACH FOR EFFICIENT MONTE CARLO COMPUTATIONS [J].

Andrieu, Christophe ;

Roberts, Gareth O. .

ANNALS OF STATISTICS, 2009, 37 (02) :697-725

[5]

[Anonymous], Sequential Monte Carlo Methods in Practice

[6] Variational Inference: A Review for Statisticians [J].

Blei, David M. ;

Kucukelbir, Alp ;

McAuliffe, Jon D. .

JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION, 2017, 112 (518) :859-877

[7] Optimization Methods for Large-Scale Machine Learning [J].

Bottou, Leon ;

Curtis, Frank E. ;

Nocedal, Jorge .

SIAM REVIEW, 2018, 60 (02) :223-311

[8]

Cappe O., 2005, SPR S STAT

[9] An overview of existing methods and recent advances in sequential Monte Carlo [J].

Cappe, Olivier ;

Godsill, Simon J. ;

Moulines, Eric .

PROCEEDINGS OF THE IEEE, 2007, 95 (05) :899-924

[10]

Chopin N., 2020, Importance Sampling

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