基于BAG感知互信息最大化的抗FDI攻击多智能体目标定位与跟踪
原标题：Resilient Multi-Agent Target Localization and Tracking via BAG-Aware Mutual Information Maximization under False Data Injection Attacks
AI 摘要
该论文提出一种在虚假数据注入（FDI）攻击下具有韧性的多智能体目标定位与跟踪框架。方法结合基于互信息的导航、基于贝叶斯攻击图（BAG）的攻击检测，以及可达集引导的恢复策略，通过信任门控隔离被攻陷智能体的传感信息，并用LQR引导未受攻击智能体重定位目标。仿真表明该框架在FDI攻击下仍能保持稳健的跟踪性能。
正文节选
Resilient Multi-Agent Target Localization and Tracking via BAG-Aware Mutual Information Maximization under False Data Injection Attacks Abstract Cooperative multi-agent networks deployed for target localization and tracking remain critically vulnerable to malicious cyber attacks, since a single compromised agent can corrupt the centralized target belief and mislead the estimation process across the entire network. This paper presents a resilient multi-agent target localization and tracking frame