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Fast Attack-Resilient Distributed State Estimator for Cyber-Physical Systems

The performance of resilient state estimators developed for cyber-physical systems (CPSs) decreases as the number of compromised sensors of the system increases. Furthermore, some of these algorithms leverage computationally expensive optimization techniques to incorporate resiliency. As such, we propose a fast resilient distributed state estimator (FRDSE), which is a novel resilient distributed algorithm that produces bounded state estimation errors regardless of the magnitude of the attack and the number of compromised sensors. Our algorithm converges to the true state in an attack-free and noise-free scenario and it produces bounded estimation errors during an attack. Compared to existing algorithms, FRDSE is more computationally efficient. We provide theoretical guarantees on the convergence of FRDSE in attack-free scenario and prove its resiliency during an attack. We demonstrate the performance of our algorithm against false data injection (FDI) attack in a platoon of vehicles and compare its runtime against existing algorithms. We observe that on a platoon of eight vehicles, runtime of our algorithm is 0.102 s, much lower than the state-of-the-art solutions.