UAV detection and recognition based on improved convolutional neural network and radio frequency fingerprint

doi:10.11772/j.issn.1001-9081.2023030299

Abstract

Abstract:

In order to solve the problems that the UAV （Unmanned Aerial Vehicle） is vulnerable to environmental interference in image recognition， and the traditional signal recognition is difficult to accurately extract features and has poor real-time performance， a UAV detection and recognition method based on improved CNN （Convolutional Neural Network） and RF （Radio Frequency） fingerprint was proposed. Firstly， a USRP （Universal Software Radio Peripheral） was used for capturing radio signals in an environment， a deviation value was obtained through multi-resolution analysis， to detect whether the radio signal was an unmanned aerial vehicle radio frequency signal or not. Secondly， the detected unmanned aerial vehicle radio frequency signal was subjected to wavelet transformation and PCA （Principal Component Analysis） to obtain a radio frequency signal spectrum which was used as an input of a neural network. Finally， a LRCNN （Lightweight Residual Convolutional Neural Network） was constructed， and the RF spectrum was input to train the network for UAV classification and recognition. Experimental results show that LRCNN can effectively detect and recognize UAV signals， and the average recognition accuracy reaches 84%. When the SNR （Signal-to-Noise Ratio） is greater than 20 dB， the recognition accuracy of LRCNN reaches 88%， which is 31 and 7 percentage points higher than those of SVM （Support Vector Machine） and the original OracleCNN， respectively. Compared with these two methods， LRCNN has improved recognition accuracy and robustness.

Key words: UAV (Unmanned Aerial Vehicle) security, radio frequency fingerprint, wavelet transform, attention residual network, Convolutional Neural Network (CNN)

摘要：

针对无人机（UAV）在图像识别时易受环境干扰，而传统信号识别难以准确提取特征且实时性较差的问题，提出一种基于改进卷积神经网络（CNN）和射频（RF）指纹的无人机检测识别方法。首先，使用通用软件无线电外设（USRP）捕获环境中的无线电信号，经过多分辨率分析获取偏差值，检测是否为无人机射频信号；其次，将检测到的无人机射频信号经过小波变换和主成分分析（PCA）处理，获得射频信号频谱，作为神经网络的输入；最后，构建轻量级残差神经网络（LRCNN），输入射频频谱进行网络训练，进行无人机的分类识别。实验结果表明，所提方法可以有效检测并识别无人机信号，平均识别精度可达84%；在信噪比（SNR）大于20 dB时，LRCNN的识别精度达到了88%，相较于支持向量机（SVM）、原始OracleCNN分别提高31和7个百分点，在识别精度和鲁棒性方面比这两种方法均有所提升。

关键词: 无人机安全, 射频指纹, 小波变换, 注意力残差网络, 卷积神经网络

CLC Number:

TP391

Jingxian ZHOU, Xina LI. UAV detection and recognition based on improved convolutional neural network and radio frequency fingerprint[J]. Journal of Computer Applications, 2024, 44(3): 876-882.

周景贤, 李希娜. 基于改进卷积神经网络和射频指纹的无人机检测与识别[J]. 《计算机应用》唯一官方网站, 2024, 44(3): 876-882.

Figures/Tables 14

Fig. 1 Recognition flow of UAV RF signal

Fig. 2 Algorithm framework for UAV radio frequency fingerprint detection and recognition

Fig. 3 Process of two-dimensional wavelet transform

Fig. 4 Signals before and after two-dimensional wavelet transform and PCA

Fig. 5 Structure of ResBlock

Fig. 6 Structure of LRCNN

Tab. 1 Parameters of LRCNN

参数	值
卷积层1	卷积核 $50 × 2 × 7$
ReLU	Max（0，x）
残差块卷积层1	卷积核 $50 × 2 × 7$
残差块卷积层2	卷积核 $50 × 2 × 7$
全局平均池化层	$2 × 2$
全连接层1（FC1）	神经元80
全连接层2（FC2）	神经元4
dropout率	50%
I2正则化参数	λ=0.000 1

Tab. 1 Parameters of LRCNN

参数	值
卷积层1	卷积核 $50 × 2 × 7$
ReLU	Max（0，x）
残差块卷积层1	卷积核 $50 × 2 × 7$
残差块卷积层2	卷积核 $50 × 2 × 7$
全局平均池化层	$2 × 2$
全连接层1（FC1）	神经元80
全连接层2（FC2）	神经元4
dropout率	50%
I2正则化参数	λ=0.000 1

Tab. 2 Parameters of Adam stochastic gradient descent algorithm

名称	含义	取值
Learning_rate	学习速率、学习步长	0.000 1
Beta1	一阶矩估计的指数衰减因子	0.990 0
Beta2	二阶矩估计的指数衰减因子	0.990 0
Epsilon	避免除0参数（≥0）	0.001 0
Use_locking	为true时，更新操作使用锁	—

Tab. 3 Training time and accuracy with different batch_size

batch_size	训练时间/min	训练精度/%
2	35	80.6
4	20	84.2
8	12	67.8

Fig. 7 Recognition accuracy varying with number of training times

Fig. 8 Ablation experiment results of LRCNN

Tab. 4 Influence of number of UAVs on recognition accuracy

无人机数	不同算法的识别精度/%
无人机数	LRCNN	OracleCNN	SVM
2	88.2	84.1	63.3
4	86.2	81.3	60.6
8	85.2	78.7	57.3
16	83.9	75.9	52.4

Fig. 9 Influence of training times on recognition accuracy

Fig. 10 Influence of SNR on recognition accuracy

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