Distributed Decision Making for Electromagnetic Radiation Source Localization Using Multi-Agent Deep Reinforcement Learning

The detection and localization of radiation sources in urban areas present significant challenges in electromagnetic spectrum operations, particularly with the proliferation of small UAVs. To address these challenges, we propose the Multi-UAV Reconnaissance Proximal Policy Optimization (MURPPO) algorithm based on a distributed reinforcement learning framework, which utilizes an independent decision making mechanism and collaborative positioning method with multiple UAVs to achieve high-precision detection and localization of radiation sources. We adopt a dual-branch actor structure for independent decisions in UAV control, which reduces decision complexity and improves learning efficiency. The algorithm incorporates task-specific knowledge into the reward function design to guide UAVs in exploring abnormal radiation sources. Furthermore, we employ a geometry-based three-point localization algorithm that leverages multiple UAVs’ spatial distribution for precise abnormal radiation source positioning. Simulations in urban environments demonstrate the effectiveness of the MURPPO algorithm, with the proportion of successfully localized target radiation sources converging to 56.5% in the later stages of training, approaching a 38.5% improvement over a traditional multi-agent proximal policy optimization algorithm. The results indicate that MURPPO effectively addresses the challenges of the intelligent sensing and localization of UAVs in complex urban electromagnetic spectrum operations.