Sequential fusion estimation for nonlinear systems under deceptive attacks

Abstract

This article deals with sequential fusion estimation for nonlinear systems under deceptive attacks, which may happen during transmissions of measurements and system states over communication networks. The estimator is developed by incorporating measurement fusion and local estimate fusion with attack detection. Moreover, a compensation strategy is designed to mitigate the impact of deceptive attacks on performance degradation. The proposed method may preserve the data integrity for accurate state estimation with deceptive attacks. Numerical simulation with the target tracking systems verifies the effectiveness of the proposed method.