Nonlinear estimation using Mean Field Games

Sergio Pequito*, A. Pedro Aguiar, Bruno Sinopoli, Diogo Gomes

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

12 Scopus citations

Abstract

This paper introduces Mean Field Games (MFG) as a framework to develop optimal estimators in some sense for a general class of nonlinear systems. We show that under suitable conditions the estimation error converges exponentially fast to zero. Computer simulations are performed to illustrate the method. In particular we provide an example where the proposed estimator converges whereas both extended Kalman filter and particle filter diverge.

Original languageEnglish (US)
Title of host publicationInternational Conference on NETwork Games, Control and Optimization, NetGCooP 2011
StatePublished - Dec 1 2011
EventInternational Conference on NETwork Games, Control and Optimization, NetGCooP 2011 - Paris, France
Duration: Oct 12 2011Oct 14 2011

Publication series

NameInternational Conference on NETwork Games, Control and Optimization, NetGCooP 2011

Other

OtherInternational Conference on NETwork Games, Control and Optimization, NetGCooP 2011
CountryFrance
CityParis
Period10/12/1110/14/11

ASJC Scopus subject areas

  • Computer Networks and Communications
  • Human-Computer Interaction
  • Control and Systems Engineering

Fingerprint

Dive into the research topics of 'Nonlinear estimation using Mean Field Games'. Together they form a unique fingerprint.

Cite this