Name
Abstract | ||
|---|---|---|
Linear discrete stochastic descriptor systems with multisensor have been transformed, using the singular value decomposition (SVD), into two reduced-order non-descriptor subsystems with multisensor. Based on the linear minimum variance optimal fusion rule weighted by diagonal matrices, a decoupled distributed Kalman fuser is presented by using the Kalman filtering method and white noise estimation theory. With this procedure it is possible to handle the fused filtering, smoothing, and prediction problems in a unified framework, and realize a decoupled fused estimation for state components. Its accuracy is higher than that of each local Kalman estimator. In order to compute the optimal weights, the formulas of computing the cross-covariances among local estimation errors have been presented. A Monte Carlo simulation example shows its effectiveness. |
Year | DOI | Venue |
|---|---|---|
2008 | 10.1016/j.sigpro.2007.11.016 | Signal Processing |
Keywords | Field | DocType |
monte carlo simulation example,white noise estimation theory,descriptor system,decoupled fused estimation,linear discrete stochastic descriptor,local kalman estimator,diagonal matrix,linear minimum variance optimal,kalman fuser,optimal weight,local estimation error,white noise,singular value decomposition,estimation theory,kalman filter,minimum variance,monte carlo simulation | Singular value decomposition,Minimum-variance unbiased estimator,Control theory,White noise,Sensor fusion,Kalman filter,Smoothing,Estimation theory,Discrete system,Mathematics | Journal |
Volume | Issue | ISSN |
88 | 5 | Signal Processing |
Citations | PageRank | References |
5 | 0.57 | 6 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yuan Gao | 1 | 88 | 7.67 |
| Gui-Li Tao | 2 | 14 | 1.95 |
| Zi-li Deng | 3 | 514 | 44.75 |