Resilient Consensus-Based Target Tracking under False Data Injection Attacks in Multi-Agent Networks

2026-08-02

AI Summary

This work addresses the vulnerability of distributed multi-agent target tracking in dynamic and adversarial environments to false data injection attacks and measurement faults. The authors propose a consensus-based robust estimation algorithm that integrates an approximate constant-velocity motion model with saturation filtering to suppress impulsive disturbances. A lightweight detection and isolation mechanism, grounded in innovation thresholds, is designed to identify and discard malicious measurements in real time. Without significantly increasing communication or computational overhead, the method enhances both system resilience against diverse attacks and estimation accuracy. Simulations demonstrate that the proposed approach accurately identifies compromised agents under both benign and adversarial conditions, preventing contamination of the global estimate, and further show that higher network connectivity and consensus iteration rates accelerate convergence and improve precision.

Abstract

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.

Problem

• resilient consensus
• target tracking
• false data injection attacks
• multi-agent networks
• distributed estimation

Innovation

• resilient consensus
• false data injection attack
• saturation-based filtering
• distributed target tracking
• innovation threshold