Performance Evaluation of Micro-Doppler Based UAV Identification Using Different 5G Frame Structures

The integrated sensing and communication (ISAC) technology is considered as a key feature in the upcoming B5G/6G networks. Currently, most ISAC research focuses on target detection and localization, with limited attention given to target identification. The lack of focus on target identification results in challenges in distinguishing small unmanned aerial vehicles (UAVs) from other birds or insects in urban low-altitude scenarios. In this study, we propose a method for extracting micro-motion feature spectrograms of UAVs using 5G signals. Unlike traditional radar systems, ISAC base stations utilize communication waveforms and time-division duplex (TDD) types with downlink and uplink slots for environmental sensing. To assess the performance of UAV identification using different TDD types, we evaluate the mean squared error in estimating rotor blade parameters for each TDD type. Simulation results demonstrate the feasibility of utilizing 5G signals to extract micro-motion features of UAVs. While all evaluated 5G TDD types show promising results in recovering rotor UAV blade parameters at high signal-to-noise ratios (SNRs), their estimation performance varies significantly at low SNRs. These findings provide a solid foundation for future research on micro-Doppler recognition in ISAC systems.