Sensing, Detection and Localization for Low Altitude UAV: A RF-Based Framework via Multiple BSs Collaboration

TL;DR

The paper tackles the challenge of sensing, detecting, and localizing low-altitude UAVs in cluttered urban environments by leveraging existing cellular base stations. It proposes a multi-layer RF framework that combines CA-CFAR-based parameter estimation with micro-Doppler signature (MDS) recognition, and fuses measurements across multiple BSs on a grid while using clustering to mitigate ghost targets. The approach is augmented with a CRLB benchmark and reinforced by an RL-based BS-selection strategy to balance localization accuracy against resource usage, validated through both numerical simulations and practical indoor experiments that achieve centimeter-level accuracy. The work offers a scalable, infrastructure-friendly solution for safe airspace management in the growing low-altitude economy, highlighting improvements from cooperative sensing and adaptive station activation.”

Abstract

The rapid growth of the low-altitude economy has resulted in a significant increase in the number of Low, slow, and small (LLS) 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. 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 station resource usage. Simulations demonstrate that increasing from one to multiple BSs reduces the positioning error to near the CRLB, while practical experiments further verify the framework's effectiveness. Furthermore, our RL-based optimization can find solutions that maintain high accuracy while minimizing resource usage, highlighting its potential as a scalable solution for ensuring airspace safety in the emerging low-altitude economy.

Figures (15)

• Figure 1: System model for multi-BS collaborative UAV detection in a complex low-altitude environment.
• Figure 2: Geometry types of multipath ghost generation.
• Figure 3: The end-to-end framework for multi-BS collaborative UAV detection and localization.
• Figure 4: Geometric model for analyzing the micro-Doppler effect from a UAV's rotor blade as observed by a BS.
• Figure 5: Determination of the possible detection region.