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Local Decomposition of Kalman Filters and its Application for Secure State Estimation.
- Source :
-
IEEE Transactions on Automatic Control . Oct2021, Vol. 66 Issue 10, p5037-5044. 8p. - Publication Year :
- 2021
-
Abstract
- This article is concerned with the secure state estimation problem of a linear discrete-time Gaussian system in the presence of sparse integrity attacks. $\mathbf {m}$ sensors are deployed to monitor the state and $\mathbf {p}$ of them can potentially be compromised by an adversary, whose data can be arbitrarily manipulated by the attacker. We show that the optimal Kalman estimate can be decomposed as a weighted sum of local state estimates. Based on these local estimates, we propose a convex optimization based approach to generate a more secure state estimate. It is proved that our proposed estimator coincides with the Kalman estimator with a certain probability when all sensors are benign. Besides, we establish a sufficient condition under which the proposed estimator is stable against the $\mathbf {(p,m)}$ -sparse attack. A numerical example is provided to validate the secure state estimation scheme. [ABSTRACT FROM AUTHOR]
- Subjects :
- *KALMAN filtering
*DISCRETE-time systems
*CYBER physical systems
*DETECTORS
Subjects
Details
- Language :
- English
- ISSN :
- 00189286
- Volume :
- 66
- Issue :
- 10
- Database :
- Academic Search Index
- Journal :
- IEEE Transactions on Automatic Control
- Publication Type :
- Periodical
- Accession number :
- 153732287
- Full Text :
- https://doi.org/10.1109/TAC.2020.3044854