恶意攻击下基于分布式稀疏优化的安全状态估计

恶意生成的量测攻击信号是导致信息物理系统(Cyber-physical system,CPS)探测失效的主要原因,如何有效削弱其影响是实现精准探测、跟踪与感知的关键问题.分布式传感器网络(Distributed sensor network,DSN)依靠多传感器协作与并行处理突破单一监测节点的任务包线,能够显著提升探测...

Secure State Estimation Based on Distributed Sparse Optimization Under Malicious Attacks

Malicious attacks against the measurements is one of the primary cause accounting for the detection failure of cyber-physical systems (CPS). Reducing the impact of measurement attack is a key problem of the accurate detection, target tracking, and sensing for CPS. Distributed sensor networks (DSN) are able to break through the task envelope of single surveillance node through coordination and parallel processing and thus remarkably improve the tracking performance and reliability of detection systems. Based on the compressive sensing theory, the state estimation for single-plant target tracking is modelled as an l₀-norm minimization problem, which is also equivalent to a sparse optimization problem. Under sparse malicious attacks, the orthogonal matching pursuit (OMP) is utilized to reconstruct the attack signals and to avoid the local optima induced by the convex optimization algorithms. A combined Kalman filter is presented to obtain the true target information where the attack signals are compensated in the measurement update. Then, a distributed secure state estimation method based on the potential game theory is proposed in view of the complex attacks such as the false data injection, where a potential game framework is established to enhance the stability of target tracking by the information exchange and coordination among neighboring sensors. Simulation results demonstrate the effectiveness of the proposed method against the sparse malicious attacks on DSNs.