Resilient Distributed Filter for State Estimation of Cyber-Physical Systems Under Attack

Abstract

Proliferation of distributed Cyber-Physical Systems has raised the need for developing computationally efficient security solutions. Toward this objective, distributed state estimators that can withstand attacks on agents (or nodes) of the system have been developed, but many of these works consider the estimation error to asymptotically converge to zero by restricting the number of agents that can be compromised. We propose Resilient Distributed Kalman Filter (RDKF), a novel distributed algorithm that estimates states within an error bound and does not depend on the number of agents that can be compromised by an attack. Our method is based on convex optimization and performs well in practice, which we demonstrate with the help of a simulation example. We theoretically show that, in a connected network, the estimation error generated by the Distributed Kalman Filter and our RDKF at each agent converges to zero in an attack free and noise free scenario. Furthermore, our resiliency analysis result shows that the RDKF algorithm bounds the disturbance on the state estimate caused by an attack.