Journal of Computer Applications ›› 2024, Vol. 44 ›› Issue (3): 876-882.DOI: 10.11772/j.issn.1001-9081.2023030299
• Network and communications • Previous Articles Next Articles
Jingxian ZHOU1, Xina LI2()
Received:
2023-03-23
Revised:
2023-05-30
Accepted:
2023-06-02
Online:
2023-06-20
Published:
2024-03-10
Contact:
Xina LI
About author:
ZHOU Jingxian, born in 1981, Ph. D., associate research fellow. His research interests include security authentication protocol, data privacy protection, security architecture for internet of things.
Supported by:
通讯作者:
李希娜
作者简介:
周景贤(1981—),男, 河南信阳人,副研究员,博士,主要研究方向:安全认证协议、数据隐私保护、物联网安全架构;
基金资助:
CLC Number:
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.
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URL: https://www.joca.cn/EN/10.11772/j.issn.1001-9081.2023030299
参数 | 值 |
---|---|
卷积层1 | 卷积核 |
ReLU | Max(0,x) |
残差块卷积层1 | 卷积核 |
残差块卷积层2 | 卷积核 |
全局平均池化层 | |
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 |
ReLU | Max(0,x) |
残差块卷积层1 | 卷积核 |
残差块卷积层2 | 卷积核 |
全局平均池化层 | |
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 |
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. 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 | — |
batch_size | 训练时间/min | 训练精度/% |
---|---|---|
2 | 35 | 80.6 |
4 | 20 | 84.2 |
8 | 12 | 67.8 |
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 |
无人机数 | 不同算法的识别精度/% | ||
---|---|---|---|
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 |
Tab. 4 Influence of number of UAVs on recognition accuracy
无人机数 | 不同算法的识别精度/% | ||
---|---|---|---|
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 |
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