Leong, Alex S., Quevedo, Daniel E., Dolz, Daniel and Dey, Subhrakanti (2019) Information Bounds for State Estimation in the Presence of an Eavesdropper. IEEE Control Systems Letters, 3 (3). pp. 547-552. ISSN 2475-1456
Preview
Information_Bounds_for_State_Estimation_in_the_Presence_of_an_Eavesdropper.pdf
Download (435kB) | Preview
Abstract
Remote state estimation problems in the presence of eavesdroppers have recently been investigated in the literature. For unstable systems, it has been shown that it is possible to keep the expected estimation error covariance bounded, while the expected eavesdropper error covariance becomes unbounded in the infinite horizon. In this note, we consider an alternative notion of security based on the amount of information revealed to the eavesdropper. Upper and lower bounds on the information revealed are derived. In particular, in the infinite horizon, it is shown that with unstable systems, any transmission policy (within the class of stationary deterministic policies where the sensor at each time step can either transmit its local state estimate or not) which keeps the expected estimation error covariance bounded must always reveal a non-zero expected amount of information to the eavesdropper.
Item Type: | Article |
---|---|
Keywords: | Security; Estimation error; Current measurement; State estimation; Mutual information; Covariance matrices; Steady-state; |
Academic Unit: | Faculty of Science and Engineering > Research Institutes > Hamilton Institute |
Item ID: | 16361 |
Identification Number: | 10.1109/LCSYS.2019.2912262 |
Depositing User: | Subhrakanti Dey |
Date Deposited: | 27 Jul 2022 08:33 |
Journal or Publication Title: | IEEE Control Systems Letters |
Refereed: | Yes |
Related URLs: | |
URI: | https://mural.maynoothuniversity.ie/id/eprint/16361 |
Use Licence: | This item is available under a Creative Commons Attribution Non Commercial Share Alike Licence (CC BY-NC-SA). Details of this licence are available here |
Repository Staff Only (login required)
Downloads
Downloads per month over past year