Problem
Research questions and friction points this paper is trying to address.
Multi-Agent Target Localization and Tracking
False Data Injection Attacks
Resilient Cooperative Networks
Cybersecurity
Resilient Multi-Agent Target Localization and Tracking via BAG-Aware Mutual Information Maximization under False Data Injection Attacks
🤖 AI Summary
This study addresses the vulnerability of multi-agent networks to false data injection (FDI) attacks, where single-point failures can cause the collapse of target localization and tracking. To mitigate this, an attack-resilient cooperative framework is proposed that integrates information navigation, Bayesian attack graph (BAG) detection, and reachable set recovery techniques. Specifically, compromised nodes are precisely isolated through BAG-based probabilistic identification combined with a trust-gating mechanism, while belief dynamics are reconstructed and trajectories are guided toward recovery via mutual information maximization and linear quadratic regulator (LQR) control. Simulation results demonstrate that the proposed framework maintains robust tracking performance under FDI attacks, providing effective autonomous resilience assurance for safety-critical applications.
📝 Abstract
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.
Innovation
Methods, ideas, or system contributions that make the work stand out.
False Data Injection Attacks
Bayesian Attack Graph
Multi-Agent Target Tracking
Mutual Information Maximization
Linear Quadratic Regulator
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