Name
Abstract | ||
|---|---|---|
For the multisensor system with different measurement matrices, correlated measurement noises and unknown noise variances, by correlated method, the online identifiers of the noise variances are obtained. Based on ARMA innovation model, a self-tuning weighted measurement fusion Kalman filter is presented, which avoids Lyapunov and Riccati equations, reduces the computational burden and is suitable for real time application. By dynamic error system analysis (DESA) method, it is rigorously proved that the proposed self-tuning fused Kalman filter converges to the corresponding optimal fused Kalman filter with probability one or in a realization, i.e. it has asymptotical global optimality. A simulation example for a target tracking systems with 3 sensors shows its effectiveness. |
Year | DOI | Venue |
|---|---|---|
2009 | 10.1109/CDC.2009.5399825 | CDC |
Keywords | Field | DocType |
multisensor system,measurement matrix,kalman filter,kalman filters,online identifiers,arma innovation model,noise,matrix algebra,target tracking systems,dynamic error system analysis method,self-tuning weighted measurement fusion,correlated measurement noise,unknown noise variance,sensor fusion,riccati equation,system analysis,noise measurement,global optimization,mathematical model | Extended Kalman filter,Alpha beta filter,Noise measurement,Fast Kalman filter,Computer science,Control theory,Sensor fusion,Kalman filter,Ensemble Kalman filter,Invariant extended Kalman filter | Conference |
ISSN | ISBN | Citations |
0191-2216 E-ISBN : 978-1-4244-3872-3 | 978-1-4244-3872-3 | 0 |
PageRank | References | Authors |
0.34 | 2 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yuan Gao | 1 | 0 | 0.34 |
| Zi-li Deng | 2 | 514 | 44.75 |
| Chenjian Ran | 3 | 24 | 4.13 |