Detection of Sensor Attack and Resilient State Estimation for Uniformly Observable Nonlinear Systems having Redundant Sensors

TL;DR

This paper develops a detection and resilient estimation method for nonlinear systems with redundant sensors, capable of identifying sensor attacks and accurately estimating states despite malicious interference.

Contribution

It introduces a novel attack detection algorithm and resilient state estimation scheme specifically designed for uniformly observable nonlinear systems with sensor redundancy.

Findings

Effective attack detection demonstrated through simulations

Resilient state estimation maintains accuracy under sensor attacks

Computationally efficient online monitoring scheme

Abstract

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.