Name
Abstract | ||
|---|---|---|
In networked systems, state estimation is hampered by communication limits. Past approaches, which consider scheduling sensors through deterministic event-triggers, reduce communication and maintain estimation quality. However, these approaches destroy the Gaussian property of the state, making it computationally intractable to obtain an exact minimum mean squared error estimate. We propose a stochastic event-triggered sensor schedule for state estimation which preserves the Gaussianity of the system, extending previous results from the single-sensor to the multi-sensor case. |
Year | DOI | Venue |
|---|---|---|
2015 | 10.1109/TAC.2015.2505066 | IEEE Trans. Automat. Contr. |
Keywords | Field | DocType |
State estimation,Yttrium,Time measurement,Covariance matrices,Current measurement,Gaussian distribution | Mathematical optimization,Scheduling (computing),Minimum mean square error,Gaussian,Mathematics | Journal |
Volume | Issue | ISSN |
abs/1502.03068 | 9 | 0018-9286 |
Citations | PageRank | References |
14 | 0.65 | 12 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Sean Weerakkody | 1 | 131 | 7.80 |
| Yilin Mo | 2 | 891 | 51.51 |
| Bruno Sinopoli | 3 | 2837 | 188.08 |
| Duo Han | 4 | 143 | 8.21 |
| Ling Shi | 5 | 1717 | 107.86 |