A mean-field control-oriented approach to particle filtering

Tao Yang, Prashant G. Mehta, Sean P. Meyn

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

A new formulation of the particle filter for nonlinear filtering is presented, based on concepts from optimal control, and from the mean-field game theory framework of Huang et. al. [8]. The optimal control is chosen so that the posterior distribution of a particle matches as closely as possible the posterior distribution of the true state, given the observations. In the infinite-N limit, the empirical distribution of ensemble particles converges to the posterior distribution of an individual particle. The cost function in this control problem is the Kullback-Leibler (K-L) divergence between the actual posterior, and the posterior of any particle. The optimal control input is characterized by a certain Euler-Lagrange (E-L) equation. A numerical algorithm is introduced and implemented in two general examples: A linear SDE with partial linear observations, and a nonlinear oscillator perturbed by white noise, with partial nonlinear observations.

Original languageEnglish (US)
Title of host publicationProceedings of the 2011 American Control Conference, ACC 2011
Pages2037-2043
Number of pages7
StatePublished - Sep 29 2011
Event2011 American Control Conference, ACC 2011 - San Francisco, CA, United States
Duration: Jun 29 2011Jul 1 2011

Publication series

NameProceedings of the American Control Conference
ISSN (Print)0743-1619

Other

Other2011 American Control Conference, ACC 2011
CountryUnited States
CitySan Francisco, CA
Period6/29/117/1/11

ASJC Scopus subject areas

  • Electrical and Electronic Engineering

Fingerprint Dive into the research topics of 'A mean-field control-oriented approach to particle filtering'. Together they form a unique fingerprint.

Cite this