Resilient Distributed Filtering for Multitarget Systems With Coupled Measurements Under Multichannel Deception Attacks
针对多目标跟踪系统中测量耦合和多通道攻击问题，提出一种改进的卡尔曼一致性滤波器，通过设计共识结构和增益项确保估计误差有界，并通过数值仿真验证有效性。
This article investigates the problem of secure state estimation for multitarget tracking systems based on Kalman consensus filtering. In the existing distributed Kalman filters, the filter gain and consensus structure rely on the independence of tracked targets, which cannot maintain the estimation performance when encountering coupled measurements across multiple targets. Moreover, the existing researches mainly focus on the security in single-channel systems, whereas such efforts fail to consider potential attacks in multichannel scenarios. In this case, by establishing a target-dependent augmented system and a link-unreliable composite directed graph, the coupling features and multichannel attacks are depicted. Then, a modified Kalman consensus filter is proposed by specifically designing consensus structure and gain terms to account for the impacts of coupled measurements and attacks. Furthermore, by scaling the Lyapunov function through the Riccati difference equation and matrix inequalities, sufficient conditions are established to ensure the boundedness of estimation errors. Numerical simulations are conducted to demonstrate the effectiveness of the filter.