Name
Abstract | ||
|---|---|---|
For the multisensor systems with unknown noise variances, using correlation method and least squares fusion criterion, information fusion noise variance estimators are presented by the average of local noise variance estimators, which have the consistence. Substituting the fused noise variance online estimators into the optimal Riccati equation and the optimal weighted measurement fusion Kalman filter, a self-tuning Riccati equation and a new self-tuning weighted measurement fusion Kalman filter are presented. In order to prove the convergence of the self-tuning Riccati equation, a dynamic variance error system analysis (DVSEA) method is presented, which converts the convergence problem to the stability problem of a time-varying Lyapunov equation. A stability decision criterion is presented for the Lyapunov equation. By the dynamic error system analysis (DESA) method and DVSEA method, it proves that the self-tuning weighted measurement fusion Kalman filter converges to the globally optimal weighted measurement fusion Kalman filter in a realization, so that it has asymptotic global optimality. A simulation example for target tracking system with 3-sensor shows its effectiveness. |
Year | DOI | Venue |
|---|---|---|
2009 | 10.1109/CDC.2009.5399610 | CDC |
Keywords | Field | DocType |
multisensor system,self-adjusting systems,kalman filter,kalman filters,correlation method,dynamic variance error system analysis,optimal riccati equation,information fusion noise variance estimator,self-tuning weighted measurement fusion,convergence analysis,time-varying lyapunov equation,convergence,least mean squares methods,stability decision criterion,riccati equations,least squares fusion criterion,asymptotic global optimality,lyapunov matrix equations,self-tuning riccati equation,correlation methods,sensor fusion,noise measurement,system analysis,lyapunov equation,riccati equation,noise,global optimization,least square | Mathematical optimization,Extended Kalman filter,Lyapunov equation,Noise measurement,Control theory,Sensor fusion,Kalman filter,Riccati equation,Invariant extended Kalman filter,Mathematics,Estimator | Conference |
ISSN | ISBN | Citations |
0191-2216 E-ISBN : 978-1-4244-3872-3 | 978-1-4244-3872-3 | 2 |
PageRank | References | Authors |
0.43 | 1 | 2 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Chenjian Ran | 1 | 24 | 4.13 |
| Zi-li Deng | 2 | 514 | 44.75 |