Name
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. |
Year | Venue | Keywords |
|---|---|---|
2011 | Network Games, Control and Optimization | Kalman filters,game theory,nonlinear estimation,particle filtering (numerical methods),MFG,extended Kalman filter,mean field games,nonlinear estimation,optimal estimator,particle filter |
Field | DocType | ISBN |
Convergence (routing),Applied mathematics,Extended Kalman filter,Nonlinear system,Control theory,Particle filter,Minimum mean square error,Kalman filter,Mean field theory,Mathematics,Estimator | Conference | 978-1-4673-0383-5 |
Citations | PageRank | References |
4 | 0.47 | 5 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Sergio Daniel Pequito | 1 | 146 | 20.72 |
| Aguiar, A. | 2 | 686 | 63.85 |
| Bruno Sinopoli | 3 | 2837 | 188.08 |
| Diogo A. Gomes | 4 | 68 | 11.86 |