## 1 Introduction  
The electromagnetic spectrum represents a finite natural resource that has taken on an increasingly crucial role in modern urban development [1]. As smart cities continue to evolve and various wireless technologies and communication systems become more widespread [2], electromagnetic spectrum monitoring and control have emerged as vital components of urban infrastructure management [3]. To ensure the smooth operation of smart cities, effective spectrum operations is essential for maintaining reliable communications and efficient city system operations. Such operation must also be capable of swiftly identifying potential sources of interference [4]. In densely populated urban areas with complex electromagnetic environments, spectrum operations continue to face unique challenges [5].  
The rapid proliferation of small UAVs has introduced new challenges to smart city management [6]. These devices, characterized by their accessibility, high mobility, and low visibility [7], pose potential threats to urban airspace management and critical infrastructure security [8]. Consequently, substantial resources are being invested in developing UAV detection and monitoring systems across various urban environments [9]. While current systems incorporate advanced sensing technologies such as signal detection [10], positioning systems, and artificial intelligence [11], developing rapid and robust closed-loop perception and positioning strategies for unauthorized radiation sources (including unregistered UAVs) in complex urban environments remains a crucial task that requires urgent attention. Given these emerging challenges, conventional single-point or static sensing [12] approaches are increasingly inadequate for complex urban environments. We need a more flexible and comprehensive solution to ensure effective spectrum operations and smart city security management.  
To tackle the above challenges, we focus on multi-UAV electromagnetic spectrum collaborative reconnaissance and countermeasures for key urban areas. The aim is to achieve a closed-loop process of the detection, localization, and tracking of abnormal radiation sources in complex electromagnetic environments. The contributions of this paper are detailed as follows:  
* Advanced Multi-UAV Framework: We develop a distributed reinforcement learning framework that enables independent decision-making while facilitating collaborative positioning among multiple UAVs. This framework specifically addresses the challenges of radiation source detection and localization in complex urban environments.
* Innovative Algorithm Design: We propose the MURPPO algorithm, an innovative approach that features a dual-branch actor structure to reduce decision complexity and improve learning efficiency. The algorithm incorporates task-specific domain knowledge into its reward function design, enabling effective guidance for UAVs in their exploration tasks. Additionally, we develop a geometry-based three-point localization method that leverages the spatial distribution of multiple UAVs to achieve precise positioning of radiation sources.
* Performance Validation: Our extensive simulations demonstrate that MURPPO achieves a 56.5% successful localization rate in later training stages, showing a 38.5% improvement over traditional multi-agent PPO algorithms with robust performance in complex urban electromagnetic environments.  
The remainder of this paper is structured as follows. Section 2 reviews related work. Section 3 introduces the system model. Section 4 provides the problem definition and analysis. Section 5 describes the proposed MURPPO algorithm. Performance analysis is presented in Section 6. Finally, Section 7 concludes the paper.