Name
Title | ||
|---|---|---|
Self-tuning fusion Kalman smoother for multisensor multi-channel ARMA signals and its convergence |
Abstract | ||
|---|---|---|
For the multisensor multi-channel autoregressive moving average (ARMA) signals with white measurement noises and an AR colored measurement noise as common disturbance noises, a multi-stage information fusion identification method is presented when model parameters and noise variances are partially unknown. The local estimators of model parameters and noise variances are obtained by the multi-dimensional recursive instrumental variable (MRIV) algorithm, correlation method, and the Gevers-Wouters algorithm, and the fused estimators are obtained by taking the average of the local estimators. They have the consistency. Substituting them into the optimal fusion Kalman smoother, a self-tuning fusion Kalman smoother for multi-channel ARMA signals is presented. Applying the dynamic error system analysis (DESA) method, it is proved that the proposed self-tuning fusion Kalman smoother converges to the optimal fusion Kalman smoother in a realization, so that it has asymptotic optimality. A simulation example shows its effectiveness. |
Year | Venue | Keywords |
|---|---|---|
2012 | Fusion | multisensor multichannel arma signals,multistage information fusion identification method,multisensor information fusion,identification,self-tuning fusion kalman smoother,smoothing methods,multisensor multi-channel autoregressive moving average signals,desa method,autoregressive moving average processes,multidimensional recursive instrumental variable algorithm,dynamic error system analysis,ar colored measurement noise,convergence analysis,convergence,gevers-wouters algorithm,white measurement noises,self-tuning kalman smoother,mriv algorithm,local estimators,sensor fusion,noise,sensors,noise measurement,kalman filters |
Field | DocType | ISBN |
Convergence (routing),Noise measurement,Computer science,Instrumental variable,Artificial intelligence,Autoregressive–moving-average model,Computer vision,Algorithm,Kalman filter,Sensor fusion,Speech recognition,Self-tuning,Estimator | Conference | 978-0-9824438-4-2 |
Citations | PageRank | References |
0 | 0.34 | 4 |
Authors | ||
2 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Gui-Li Tao | 1 | 14 | 1.95 |
| Zi-li Deng | 2 | 514 | 44.75 |