多智能体网络中针对虚假数据注入攻击的弹性共识型目标跟踪
Resilient Consensus-Based Target Tracking under False Data Injection Attacks in Multi-Agent Networks
AI总结：
本研究针对多智能体网络的虚假数据注入攻击，提出整合近常速度模型与饱和滤波的共识估计算法，搭配动态检测隔离机制，提升了分布式目标跟踪的精度与弹性。
AI中文摘要：
多智能体网络中的分布式目标跟踪在协同感知与自主导航中发挥着关键作用，但在高度动态和对抗性环境下面临重大挑战。本研究旨在提升去中心化目标跟踪算法对测量故障与网络物理威胁（尤其是虚假数据注入攻击）的弹性。我们提出一种基于共识的估计算法，整合近常速度模型与饱和滤波，以抑制脉冲式测量波动并促进稳健的分布式状态估计；为应对对抗性条件，我们引入动态虚假数据注入检测与隔离机制，该机制利用创新阈值识别并丢弃可疑测量，避免其降低全局估计性能。通过一系列良性与对抗性条件下的仿真案例研究验证所提算法的有效性，结果显示：更高的网络连通性与共识迭代速率可提升估计精度与收敛速度，而经合理调优的饱和滤波器能在故障抑制与准确估计间实现实用平衡；此外，在局部化、协同性及瞬态虚假数据注入攻击下，该检测机制可成功识别受感染智能体，防止其数据破坏分布式全局估计。总体而言，本研究表明所提算法提供了一种简化的容错方案，可显著提升分布式目标跟踪的精度与弹性，且不会带来过重的通信或计算负担。
英文摘要：
Distributed target tracking in multi-agent networks plays a critical role in cooperative sensing and autonomous navigation. However, it faces significant challenges in highly dynamic and adversarial setups. This study aims to enhance the resilience of decentralized target tracking algorithms against measurement faults and cyber-physical threats, especially false data injection attacks. We propose a consensus-based estimation algorithm that integrates a nearly-constant-velocity model with saturation-based filtering to suppress impulsive measurement variations and promote robust, distributed state estimation. To counteract adversarial conditions, we incorporate a dynamic false data injection detection and isolation mechanism that uses innovation thresholds to identify and disregard suspicious measurements before they can degrade the global estimate. The effectiveness of the proposed algorithms is demonstrated through a series of simulation-based case studies under both benign and adversarial conditions. The results show that increased network connectivity and higher consensus iteration rates improve estimation accuracy and convergence speed, while properly tuned saturation filters achieve a practical balance between fault suppression and accurate estimation. Furthermore, under localized, coordinated, and transient false data injection attacks, the detection mechanism successfully identifies compromised agents and prevents their data from corrupting the distributed global estimate. Overall, this study illustrates that the proposed algorithm provides a simplified fault-tolerant solution that significantly enhances the accuracy and resilience of distributed target tracking without imposing excessive communication or computational burdens.
