Secure State Estimation against Sensor Attacks in the Presence of Noise
This addresses the critical issue of robust state estimation in safety-critical systems like autonomous vehicles or industrial control, where sensor attacks can compromise security, representing a strong specific gain in secure control systems.
The paper tackles the problem of estimating the state of a noisy linear dynamical system when an unknown subset of sensors is arbitrarily corrupted by an adversary, proposing a secure state estimation algorithm that achieves optimal bounds on state estimation error given an upper bound on attacked sensors.
We consider the problem of estimating the state of a noisy linear dynamical system when an unknown subset of sensors is arbitrarily corrupted by an adversary. We propose a secure state estimation algorithm, and derive (optimal) bounds on the achievable state estimation error given an upper bound on the number of attacked sensors. The proposed state estimator involves Kalman filters operating over subsets of sensors to search for a sensor subset which is reliable for state estimation. To further improve the subset search time, we propose Satisfiability Modulo Theory based techniques to exploit the combinatorial nature of searching over sensor subsets. Finally, as a result of independent interest, we give a coding theoretic view of attack detection and state estimation against sensor attacks in a noiseless dynamical system.