Secure State Estimation Under Sparse Sensor Attacks Via Saturating Adaptive Technique

This article is concerned with the secure state estimation problem of cyber-physical systems (CPSs) in the presence of sparse sensor attacks. Considering that such a problem is a combinatorial problem in nature, novel saturating adaptive algorithms are proposed to reconstruct the system state from the corrupted measurements. Through introducing saturating adaptive terms limiting the impact of attacks, state estimates are obtained without brute force search, and the computational complexity is reduced greatly. Meanwhile, it is shown that the proposed method also can be adopted to estimate the system state under the attacks with varying attack targets. Finally, simulation results are provided to illustrate the effectiveness of the proposed methods.