无线电工程2025，Vol.55Issue(9)：1835-1846,12.DOI:10.3969/j.issn.1003-3106.2025.09.011
基于深度学习的无人机指纹识别
UAV Fingerprint Recognition Based on Deep Learning
摘要
Abstract
The widespread application of UAV technology has imposed higher requirements on its safety and reliability.Recognition technology based on fingerprint features has become a key means for the management and control of illegal UAVs.To systematically review the research status of UAV fingerprint recognition methods based on deep learning,existing neural network architectures,problems in datasets and their optimizations,as well as the improvement of adaptability in complex environments have been focused on.Firstly,the current application status,advantages and improvements of deep learning architectures such as Convolutional Neural Network(CNN)and Recurrent Neural Network(RNN)in feature extraction and classification of UAV signals have been introduced.Secondly,the existing problems of training datasets have been studied and analyzed,the challenges of insufficient total samples and imbalanced sample proportions among categories have been summarized,and the data augmentation techniques for addressing these problems have been analyzed.Finally,the advantages and approaches of deep learning technologies in enhancing the adaptability of UAV fingerprint recognition under complex environments such as noise interference,hardware defects,and specific terrains have been summarized.The advantages and improvement directions of existing architectures,the core bottlenecks of datasets and their solution paths,as well as the enhancement strategies under complex environments have been clearly pointed out.This study can provide important references for the application and development of deep learning in UAV fingerprint recognition.关键词
深度学习/无人机/指纹识别/神经网络Key words
deep learning/UAV/fingerprint recognition/neural network分类
信息技术与安全科学引用本文复制引用
李铭典,李乐文,申安泽,刘晨晖,潘泉,李扬..基于深度学习的无人机指纹识别[J].无线电工程,2025,55(9):1835-1846,12.基金项目
陕西省重点研发计划(2024CY2-GJHX-23)Key Research and Development Plan of Shaanxi Province(2024CY2-GJHX-23) （2024CY2-GJHX-23）
