Abstract: With the rapid development of artificial intelligence and communication technologies,unmanned aerial vehicles(UAVs) are increasingly being applied across various industries.In the field of low-altitude logistics and transportation,UAVs demonstrate significant potential due to their efficiency,convenience,and low cost.However,legitimate UAVs performing delivery tasks are highly susceptible to spoofing attacks.Shippers relying solely on visual characteristics,especially when malicious third parties use UAVs of the same model as legitimate ones,find it difficult to accurately determine whether an incoming UAV is authorized to pick up a package.To effectively address this issue,an individual UAV identification system based on voiceprint features is proposed.Firstly,audio of a hovering UAV is recorded using a mobile device,and empirical wavelet transform is applied to remove high-frequency noise from the UAV's audio signal,thereby improving the signal-to-noise ratio.Then,a filter bank is designed based on the spectral characteristics of audio signals from different UAVs of the same model,enabling efficient extraction of key audio features.Finally,a long short-term memory network model incorporating the Open-Max algorithm is constructed to handle open-set classification problems,further enhancing the system's recognition capability.Experimental results demonstrate that the proposed system achieves an accuracy of 99.8% in identifying individual UAVs of the same model and a success rate of 99.5% in detecting unauthorized UAVs,effectively mitigating spoofing attacks.

Key words: Unmanned aerial vehicle, Voiceprint, Identity recognition, Security, Logistics and delivery