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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.
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https://hal.inria.fr/hal-00643677
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Submitted on : Tuesday, November 22, 2011 - 3:33:24 PM
Last modification on : Friday, August 5, 2022 - 12:39:54 PM
Long-term archiving on: : Friday, November 16, 2012 - 11:46:27 AM

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  • HAL Id : hal-00643677, version 1

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Sergio Pequito, Pedro A. Aguiar, Bruno Sinopoli, Diogo A. Gomes. Nonlinear Estimation using Mean Field Games. NetGCOOP 2011 : International conference on NETwork Games, COntrol and OPtimization, Telecom SudParis et Université Paris Descartes, Oct 2011, Paris, France. ⟨hal-00643677⟩

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