Detection of Sensor Attack and Resilient State Estimation for Uniformly Observable Nonlinear Systems having Redundant Sensors
For control systems relying on sensor measurements, this work addresses the problem of detecting and mitigating sensor attacks, but the approach is incremental and limited to a specific class of nonlinear systems.
This paper proposes a detection algorithm for sensor attacks and a resilient state estimation scheme for uniformly observable nonlinear systems with redundant sensors. Simulation results demonstrate the effectiveness of the proposed algorithm.
This paper presents a detection algorithm for sensor attacks and a resilient state estimation scheme for a class of uniformly observable nonlinear systems. An adversary is supposed to corrupt a subset of sensors with the possibly unbounded signals, while the system has sensor redundancy. We design an individual high-gain observer for each measurement output so that only the observable portion of the system state is obtained. Then, a nonlinear error correcting problem is solved by collecting all the information from those partial observers and exploiting redundancy. A computationally efficient, on-line monitoring scheme is presented for attack detection. Based on the attack detection scheme, an algorithm for resilient state estimation is provided. The simulation results demonstrate the effectiveness of the proposed algorithm.