Song Xiulan, Luo Xiaoxin, Zhu Junwei, He Defeng
College of Information Engineering, Zhejiang University of Technology, Hangzhou 310023, China.
Sensors (Basel). 2020 Feb 25;20(5):1253. doi: 10.3390/s20051253.
This paper considers the state estimation problem of intelligent connected vehicle systems under the false data injection attack in wireless monitoring networks. We propose a new secure state estimation method to reconstruct the motion states of the connected vehicles equipped with cooperative adaptive cruise control (CACC) systems. First, the set of CACC models combined with Proportion-Differentiation (PD) controllers are used to represent the longitudinal dynamics of the intelligent connected vehicle systems. Then the notion of sparseness is employed to model the false data injection attack of the wireless networks of the monitoring platform. According to the corrupted data of the vehicles' states, the compressed sensing principle is used to describe the secure state estimation problem of the connected vehicles. Moreover, the norm optimization problem is solved to reconstruct the motion states of the vehicles based on the orthogonaldecomposition. Finally, the simulation experiments verify that the proposed method can effectively reconstruct the motion states of vehicles for remote monitoring of the intelligent connected vehicle system.
本文研究了无线监测网络中存在虚假数据注入攻击情况下智能网联车辆系统的状态估计问题。我们提出了一种新的安全状态估计方法，用于重构配备协同自适应巡航控制（CACC）系统的网联车辆的运动状态。首先，结合比例-微分（PD）控制器的CACC模型集用于表示智能网联车辆系统的纵向动力学。然后，利用稀疏性概念对监测平台无线网络的虚假数据注入攻击进行建模。根据车辆状态的损坏数据，采用压缩感知原理描述网联车辆的安全状态估计问题。此外，通过求解范数优化问题，基于正交分解重构车辆的运动状态。最后，仿真实验验证了所提方法能够有效地重构车辆的运动状态，以实现对智能网联车辆系统的远程监测。
