Name
Abstract | ||
|---|---|---|
In the multiple sensor/sub-processor system, distributed estimation fusion based on the two level optimization strategy (optimal sensor estimations and optimal processor center fusion) are used widely. Optimal distributed estimation fusion with out-of-sequence measurements (OOSM) at local sensors is presented in this paper, its performance is equivalent to that of the corresponding Kalman filtering using all sensor observations (which is called the centralized Kalman filtering fusion). |
Year | DOI | Venue |
|---|---|---|
2008 | 10.1109/ICESS.2008.34 | ICESS |
Keywords | Field | DocType |
local sensor,out-of-sequence measurement,corresponding kalman,out-of-sequence measurements,multisensor estimation fusion,level optimization strategy,optimal sensor estimation,estimation fusion,centralized kalman,sensor observation,multiple sensor,optimal processor center fusion,time measurement,mathematical model,kalman filtering,kalman filter,mathematics,estimation,noise,estimation theory,sensor fusion,radar tracking,filtering,kalman filters | Computer science,Filter (signal processing),Fusion,Kalman filter,Sensor fusion,Real-time computing,Estimation theory | Conference |
ISSN | Citations | PageRank |
2576-3504 | 1 | 0.36 |
References | Authors | |
0 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Donghua Wang | 1 | 2 | 0.72 |
| yunmin zhu | 2 | 557 | 67.35 |
| xiaojing shen | 3 | 194 | 21.66 |