Name
Abstract | ||
|---|---|---|
This paper investigates the secure state estimation problem for a continuous-time Gauss-Markov system, where the physical plant is observed by m sensors and a subset of the sensors can potentially be compromised by an adversary. Under mild assumptions, we prove that the continuous-time optimal Kalman estimate can be decomposed as a weighted sum of local state estimates, each of which is computed using only the measurements from a single sensor. Then a convex optimization based approach is proposed to generate a more secure state estimate based on these local estimates. We provide a sufficient condition under which the proposed estimator is stable against the attack when less than half of the sensors are compromised. Finally, a numerical example is provided to illustrate the performance of the proposed secure state estimation scheme. |
Year | Venue | Field |
|---|---|---|
2017 | 2017 13TH IEEE CONFERENCE ON AUTOMATION SCIENCE AND ENGINEERING (CASE) | Secure state,Mathematical optimization,Computer science,Kalman filter,Convex function,Security analysis,Cyber-physical system,Physical plant,Convex optimization,Estimator |
DocType | ISSN | Citations |
Conference | 2161-8070 | 0 |
PageRank | References | Authors |
0.34 | 0 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Xinghua Liu | 1 | 76 | 14.84 |
| Yilin Mo | 2 | 891 | 51.51 |
| Xiaoqiang Ren | 3 | 58 | 12.21 |