Event-triggered projected Luenberger observer for linear systems under sparse sensor attacks

We consider the problem of designing a Luenberger-like observer for linear systems whose sensor measurements are corrupted by a malicious attacker. The attacker capabilities are limited in the sense that only a subset of all the sensors can be attacked although this subset is unknown. This leads to the problem of reconstructing the system state when the measurements are corrupted by sparse noise and we propose an observer that recursively updates the state estimate as new measurements become available. We show that by utilizing event-triggered techniques, the proposed observer is computationally more efficient than previously reported solutions to the secure state reconstruction problem.