发表机构
The University of Texas at Arlington（阿灵顿得克萨斯大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对多智能体目标定位跟踪易受虚假数据注入攻击的问题，提出结合信息导航、BAG攻击检测与可达集恢复的弹性框架，仿真验证其攻击下稳健跟踪性能。
AI 中文摘要
用于目标定位与跟踪的协作多智能体网络在恶意网络攻击下仍然极其脆弱，因为单个被攻破的智能体可能破坏集中式目标信念，并误导整个网络的估计过程。本文提出了一种在有限智能体感知范围和虚假数据注入（FDI）攻击下的弹性多智能体目标定位与跟踪框架。所提方法结合了基于信息的导航以进行目标搜索和后验目标状态不确定性降低、基于贝叶斯攻击图（BAG）的攻击检测以概率性识别被攻破的智能体，以及可达集引导的恢复机制，该机制在唯一定位智能体受到攻击时引导未受攻破的智能体重新定位目标。该框架通过信任门控被攻破的感知信息，并利用线性二次调节器（LQR）引导免疫攻击的智能体朝向动态构建的恢复集（代表新的目标信念），从而在攻击后保持网络定位能力。仿真验证表明，所提框架在FDI攻击下保持了稳健的跟踪性能，展示了在安全关键应用中弹性自主的潜力。
英文摘要
Cooperative multi-agent networks deployed for target localization and tracking remain critically vulnerable to malicious cyberattacks, 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 framework under a limited agent sensing range and false-data injection (FDI) attacks. The proposed method combines information-based navigation for target search and posterior target state uncertainty reduction, Bayesian attack graph (BAG) based attack detection for probabilistic identification of the compromised agents, and reachable set-guided recovery that guides the uncompromised agents to relocalize the target when the sole localizing agent is attacked. The framework preserves the network localization capability after an attack by trust-gating compromised sensing information and guiding attack-immune agents toward a dynamically constructed recovery set representing the new target belief using a Linear Quadratic Regulator (LQR). Simulation validates that the proposed framework maintains a robust tracking performance under FDI attacks, demonstrating the potential for resilient autonomy in safety-critical applications.
Comments6 pages, 5 figures, MECC conference (accepted)
