Unmanned Aerial Vehicles Detection and Recognition Method Based on Mel Frequency Cepstral Coefficients
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摘要: 近年来无人机(UAV)数量的剧增，无论是在民用还是军用领域都带来了一定的隐私和安全问题，因此对无人机的管控技术已成为研究热点。当前基于深度学习的射频指纹识别(RFFI)技术虽然在无人机识别上表现优异，但由于模型复杂度高，训练速度慢，且在不同数据分布下的泛化能力有限，因此在实际应用中存在局限性。该文提出一种基于梅尔频率倒谱系数的无人机识别方法，使用USRP N210采集无人机图传信号，然后提取梅尔倒谱系数(MFCC)作为无人机射频指纹特征，输入门控循环单元(GRU)进行分类识别，最后通过正则化正交匹配追踪算法(ROMP)估计无人机定位参数得到无人机具体位置。试验结果表明无人机的识别准确率可达98%，且GRU模型参数量只有1.6 k，训练时间仅需9 s，显著降低了模型复杂度并提高了训练速度和识别精度，在无人机定位中，其3维定位误差小于1 m。为进一步验证该文所提方法的可行性，对同一厂家同一型号10个无线模块进行不同距离的分类识别，1 m, 2 m, 3 m和5 m识别结果分别为100%, 98%, 98%和99%。Abstract:
Objective The widespread adoption of Unmanned Aerial Vehicles (UAVs) across civilian and military domains has introduced significant privacy and security challenges. Robust UAV identification and localization technologies are essential to address these concerns. While Radio Frequency Fingerprint Identification (RFFI) techniques based on deep learning show promise, their practical deployment is hindered by excessive model complexity, prolonged training periods, and limited generalization capabilities. This research presents a novel UAV identification and localization methodology utilizing Mel-Frequency Cepstral Coefficients (MFCC) and Gated Recurrent Unit (GRU) architecture that achieves superior accuracy with enhanced computational efficiency. Methods The proposed framework comprises several key components: (1) UAV video transmission signal acquisition via USRP N210 software-defined radio platform; (2) MFCC feature extraction to characterize distinctive radio frequency fingerprints; (3) GRU-based classification for UAV identification; and (4) Regularized Orthogonal Matching Pursuit (ROMP) algorithm implementation for three-dimensional localization parameter estimation. Comprehensive experimental evaluation assessed classification accuracy, computational complexity, training efficiency, and localization precision. Results and Discussions Experimental validation demonstrates that the proposed methodology achieves 98% UAV identification accuracy. The implemented GRU architecture contains only 1.6 k parameters and requires merely 9 seconds for training completion, representing significant reductions in model complexity and computational overhead ( Table 2 ). For localization tasks, the system achieves three-dimensional positioning error below 1 meter. Robustness assessment through classification tests on 10 identical wireless modules from the same manufacturer at varying distances (1 m, 2 m, 3 m, and 5 m) yielded identification accuracies of 100%, 98%, 98%, and 99%, respectively (Table 3 ). These results confirm the method’s exceptional performance in both identification and localization applications.Conclusions This research introduces an efficient and accurate UAV identification and localization methodology based on MFCC features and GRU architecture. The approach substantially reduces model complexity and training requirements while maintaining high identification accuracy and precise localization capabilities. Experimental validation confirms its feasibility and robustness for practical deployment. Future research directions include algorithm optimization for real-time processing and extension to diverse UAV platforms and operational environments. -
表 2 不同分类算法对比表
方法 识别精度(%) 参数量(k) 训练时间(s) KNN 93.3 – – SVM 88.7 – – CNN 96.0 17.5 20 LSTM 96.6 7.5 16 GRU 98.0 1.6 9 
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表 4 飞行高度20 m算法对比表
算法 置信度(%) 2维误差(m) 3维误差(m) OMP 50 0.30 0.65 ROMP 50 0.30 0.55 OMP
ROMP60
600.35
0.350.70
0.60
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表 5 飞行高度30 m算法对比表
算法 置信度(%) 2维误差(m) 3维误差(m) OMP 50 0.30 0.60 ROMP 50 0.30 0.45 OMP
ROMP60
600.35
0.350.65
0.60
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表 6 飞行高度50 m算法对比表
算法 置信度(%) 2维误差(m) 3维误差(m) OMP 50 0.25 0.65 ROMP 50 0.27 0.45 OMP
ROMP60
600.29
0.350.95
0.55
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