An Overview of Existing Methods and Recent Advances in Sequential Monte Carlo

Author

Cappé, Olivier ; Godsill, Simon J. ; Moulines, Eric

Author_Institution

Telecom Paris, Paris

Volume

95

Issue

5

fYear

2007

fDate

5/1/2007 12:00:00 AM

Firstpage

899

Lastpage

924

Abstract

It is now over a decade since the pioneering contribution of Gordon (1993), which is commonly regarded as the first instance of modern sequential Monte Carlo (SMC) approaches. Initially focussed on applications to tracking and vision, these techniques are now very widespread and have had a significant impact in virtually all areas of signal and image processing concerned with Bayesian dynamical models. This paper is intended to serve both as an introduction to SMC algorithms for nonspecialists and as a reference to recent contributions in domains where the techniques are still under significant development, including smoothing, estimation of fixed parameters and use of SMC methods beyond the standard filtering contexts.

Keywords

Monte Carlo methods; filtering theory; parameter estimation; Bayesian dynamical models; SMC algorithms; image processing; parameter estimation; sequential Monte Carlo; signal processing; Computer vision; Computerized monitoring; Filtering; Hidden Markov models; Monte Carlo methods; Pollution; Predictive models; Probability density function; Signal processing; Sliding mode control; Bayesian dynamical model; filtering, prediction, and smoothing; hidden Markov models; parameter estimation; particle filter; sequential Monte Carlo; state-space model; tracking;

fLanguage

English

Journal_Title

Proceedings of the IEEE

Publisher

ieee

ISSN

0018-9219

Type

jour

DOI

10.1109/JPROC.2007.893250

Filename

4266870