Efficient Secure State Estimation against Sparse Integrity Attack for System with Non-derogatory Dynamics

Abstract: We consider the problem of estimating the state of a time-invariant linear Gaussian system in the presence of integrity attacks. The attacker can compromise $p$ out of $m$ sensors, the set of which is fixed over time and unknown to the system operator, and manipulate the measurements arbitrarily. Under the assumption that all the unstable eigenvalues of system matrix $A$ have geometric multiplicity 1 (unstable part of $A$ is non-derogatory), we propose a secure estimation scheme that is resilient to integrity attack as long as the system is $2p$-sparse detectable, which is proved to be the fundamental limit of secure dynamic estimation. In the absence of attack, the proposed estimation coincides with Kalman estimation with a certain probability that can be adjusted to trade-off between performance with and without attack. Furthermore, the detectability condition checking in the designing phase and the estimation computing in the online operating phase are both computationally efficient. A numerical example is provided to corroborate the results and illustrate the performance of the proposed estimator.