Deception attacks on event-triggered distributed consensus estimation for nonlinear systems - Ren, Hongru; Cheng, Zhijian; Qin, Jiahu; Lu, Renquan (Automatica (Oxford), 2023)
This paper considers the security problem of distributed consensus estimation for nonlinear systems subject to deception attacks with enough/limited energy. Depending on whether the communication resources of the attacker are limited, two different attack strategies are introduced under the distributed consensus estimation framework. Then an event-triggered distributed consensus extended Kalman filter is devised for nonlinear systems, where the upper bound for the estimation error covariance matrix is derived in terms of the variance-constrained method. The stability analysis is executed to establish sufficient conditions that guarantee the boundedness of the estimation error. Finally, the results of a numerical simulation are analyzed to show the estimation performances of the proposed distributed consensus algorithm.