UAV Detection and Localization: A RF-Based Framework via Multiple Stations Collaboration

The rapid growth of the low-altitude economy has resulted in a significant increase in the number of low, slow, and small (LSS) unmanned aerial vehicles (UAVs), raising critical challenges for secure airspace management and reliable trajectory planning. To address this, this paper proposes a cooperative radio-frequency (RF) detection and localization framework that leverages existing cellular base stations (BSs). The proposed approach features a robust scheme for LSS target identification, integrating a cell averaging-constant false alarm rate (CA-CFAR) detector with a micro- Doppler signature (MDS) based recognition method. Multi-station measurements are fused through a grid-based probabilistic algorithm combined with clustering techniques, effectively mitigating ghost targets and improving localization accuracy in multi-UAV scenarios. Furthermore, the Cramer-Rao lower bound (CRLB) is derived as a performance benchmark and reinforcement learning (RL)-based optimization is employed to balance localization accuracy against involved BS number. Simulation results demonstrate that increasing from one to multiple BSs can reduce the positioning error to near the CRLB, while practical experiments further verify the effectiveness of our framework. Furthermore, the proposed RL-based optimization can maintain high accuracy while minimizing resource usage, highlighting its potential as a scalable solution for ensuring airspace safety in the emerging low-altitude economy.