Nonlinear estimation using Mean Field Games

Sergio Pequito; A. Pedro Aguiar; Bruno Sinopoli; Diogo A. Gomes

Nonlinear estimation using Mean Field Games

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

^*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-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 language	English (US)
Title of host publication	International Conference on NETwork Games, Control and Optimization, NetGCooP 2011
State	Published - 2011
Externally published	Yes
Event	International Conference on NETwork Games, Control and Optimization, NetGCooP 2011 - Paris, France Duration: Oct 12 2011 → Oct 14 2011

Publication series

Name	International Conference on NETwork Games, Control and Optimization, NetGCooP 2011

Other

Other	International Conference on NETwork Games, Control and Optimization, NetGCooP 2011
Country/Territory	France
City	Paris
Period	10/12/11 → 10/14/11

ASJC Scopus subject areas

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

Cite this

@inproceedings{741caaa9b88e465e868798a0ddb03262,

title = "Nonlinear estimation using Mean Field Games",

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.",

author = "Sergio Pequito and Aguiar, {A. Pedro} and Bruno Sinopoli and Gomes, {Diogo A.}",

year = "2011",

language = "English (US)",

isbn = "9781467303842",

series = "International Conference on NETwork Games, Control and Optimization, NetGCooP 2011",

booktitle = "International Conference on NETwork Games, Control and Optimization, NetGCooP 2011",

note = "International Conference on NETwork Games, Control and Optimization, NetGCooP 2011 ; Conference date: 12-10-2011 Through 14-10-2011",

}

Pequito, S, Aguiar, AP, Sinopoli, B & Gomes, DA 2011, Nonlinear estimation using Mean Field Games. in International Conference on NETwork Games, Control and Optimization, NetGCooP 2011., 6103897, International Conference on NETwork Games, Control and Optimization, NetGCooP 2011, International Conference on NETwork Games, Control and Optimization, NetGCooP 2011, Paris, France, 10/12/11.

Nonlinear estimation using Mean Field Games. / Pequito, Sergio; Aguiar, A. Pedro; Sinopoli, Bruno et al.
International Conference on NETwork Games, Control and Optimization, NetGCooP 2011. 2011. 6103897 (International Conference on NETwork Games, Control and Optimization, NetGCooP 2011).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Nonlinear estimation using Mean Field Games

AU - Pequito, Sergio

AU - Aguiar, A. Pedro

AU - Sinopoli, Bruno

AU - Gomes, Diogo A.

PY - 2011

Y1 - 2011

N2 - 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.

AB - 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.

UR - http://www.scopus.com/inward/record.url?scp=84855756276&partnerID=8YFLogxK

M3 - Conference contribution

AN - SCOPUS:84855756276

SN - 9781467303842

T3 - International Conference on NETwork Games, Control and Optimization, NetGCooP 2011

BT - International Conference on NETwork Games, Control and Optimization, NetGCooP 2011

T2 - International Conference on NETwork Games, Control and Optimization, NetGCooP 2011

Y2 - 12 October 2011 through 14 October 2011

ER -

Nonlinear estimation using Mean Field Games

Abstract

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ASJC Scopus subject areas

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