Name
Abstract | ||
|---|---|---|
We propose an open-loop and a closed-loop stochastic event-triggered sensor schedule for remote state estimation. Both schedules overcome the essential difficulties of existing schedules in recent literature works where, through introducing a deterministic event-triggering mechanism, the Gaussian property of the innovation process is destroyed which produces a challenging nonlinear filtering problem that cannot be solved unless approximation techniques are adopted. The proposed stochastic event-triggered sensor schedules eliminate such approximations. Under these two schedules, the MMSE estimator and its estimation error covariance matrix at the remote estimator are given in a closed-form. Simulation studies demonstrate that the proposed schedules have better performance than periodic ones with the same sensor-to-estimator communication rate. |
Year | DOI | Venue |
|---|---|---|
2014 | 10.1109/TAC.2015.2406975 | Automatic Control, IEEE Transactions |
Keywords | DocType | Volume |
Schedules,Kalman filters,State estimation,Technological innovation,Covariance matrices,Standards | Journal | PP |
Issue | ISSN | Citations |
99 | 0018-9286 | 60 |
PageRank | References | Authors |
1.76 | 24 | 6 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Duo Han | 1 | 143 | 8.21 |
| Yilin Mo | 2 | 891 | 51.51 |
| Junfeng Wu | 3 | 428 | 33.16 |
| Sean Weerakkody | 4 | 131 | 7.80 |
| Bruno Sinopoli | 5 | 2837 | 188.08 |
| Ling Shi | 6 | 1717 | 107.86 |