Name
Abstract | ||
|---|---|---|
In this paper we consider the problem of infinite-horizon sensor scheduling for estimation in linear Gaussian systems. Due to possible channel capacity, energy budget or topological constraints, it is assumed that at each time step only a subset of the available sensors can be selected to send their observations to the fusion center, where the state of the system is estimated by means of a Kalman filter. Several important properties of the infinite-horizon schedules will be presented in this paper. In particular, we prove that the infinite-horizon average estimation error and the boundedness of a schedule are independent of the initial covariance matrix. We further provide a constructive proof that any feasible schedule with finite average estimation error can be arbitrarily approximated by a bounded periodic schedule. We later generalized our result to lossy networks. These theoretical results provide valuable insights and guidelines for the design of computationally efficient sensor scheduling policies. |
Year | DOI | Venue |
|---|---|---|
2014 | 10.1016/j.sysconle.2014.02.002 | Systems & Control Letters |
Keywords | Field | DocType |
Wireless sensor networks,Kalman filtering,Riccati equation | Mathematical optimization,Constructive proof,Control theory,Scheduling (computing),Kalman filter,Gaussian,Schedule,Covariance matrix,Wireless sensor network,Channel capacity,Mathematics | Journal |
Volume | ISSN | Citations |
67 | 0167-6911 | 10 |
PageRank | References | Authors |
0.74 | 15 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yilin Mo | 1 | 891 | 51.51 |
| Emanuele Garone | 2 | 324 | 38.77 |
| Bruno Sinopoli | 3 | 2837 | 188.08 |