Distributed Localization and Encirclement of Unknown Eavesdroppers in Multi-Agent Systems

This paper addresses the problem of locating and suppressing a passive eavesdropper in a multi-agent system (MAS) with unknown adversary position. Unlike existing works that assume the eavesdropper's location is known or detectable, we consider a more realistic scenario where the agents must infer the eavesdropper's position based solely on the intensity of intercepted signals. To exploit the distance-dependent nature of signal strength, we develop a distributed unscented Kalman filter (DUKF) that enables each agent to estimate the eavesdropper's position using noisy, partial observations and local communication. Once localization is achieved, a control strategy based on the separation principle is applied to guide agents in encircling the estimated position cooperatively. The entire framework is fully decentralized, scalable, and suitable for operation in adversarial environments. Simulation results using a satellite formation system verify the effectiveness of the proposed approach in achieving accurate localization and coordinated encirclement under measurement uncertainty.