Research on Open-Set Radio Frequency Fingerprint Identification Based on Transfer Learning and Improved OpenMax Algorithm

doi:10.3969/j.issn.1671-1122.2026.06.005

Abstract

Abstract:

At present, most research on radio frequency fingerprinting identification predominantly operate under closed-set assumptions, with less consideration given to the occurrence of unknown samples in the test set. radio frequency signals often require the use of high-precision receivers for collection, making it difficult to obtain high-quality labeled samples. Therefore, the article proposed a method of open-set radio frequency fingerprint identification based on transfer learning and improved OpenMax algorithm. This method modified the input channels of the ResNet18 model to adapt to the grayscale properties of spectrograms and fine-tunes pre-trained parameters during training, achieving cross-domain model transfer from image to spectrogram and finally signal domain. It solved the problems of large sample size requirements and difficulties in practical signal acquisition in the application of deep learning algorithms to radio frequency fingerprint identification. At the same time, Bayesian optimization algorithm was introduced into OpenMax algorithm to solve the problem of manually setting multiple parameters such as confidence threshold and tail number in the original algorithm. In addition, introducing multi-scale tail factors for adaptive distribution fitting of samples of different categories further improves the performance of the model. Experiments demonstrate that the proposed method achieves robust classification performance across varying quantities of unknown samples, with accuracy, recall and F1 score all exceeding 99%, and has good generalization performance.

Key words: radio frequency fingerprint, open-set recognition, transfer learning, OpenMax

CLC Number:

TP309

ZHOU Xueguang, ZHAO Yue, CHEN Lu, MAO Yihuan, YAN Meishuang. Research on Open-Set Radio Frequency Fingerprint Identification Based on Transfer Learning and Improved OpenMax Algorithm[J]. Netinfo Security, 2026, 26(6): 899-912.

Figures/Tables 11

References 33

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网络层	卷积核大小/通道	步长	输出
输入	—	—	224×224×1
Conv1	7×7,64	2	112×112×64
Maxpool	3×3,64	2	56×56×64
残差层1	$\left[ \begin{align} & 3\times 3,64 \\ & 3\times 3,64 \\ \end{align} \right]\times 2 $	1	56×56×64
残差层2	$\left[ \begin{align} & 3\times 3,128 \\ & 3\times 3,128 \\ \end{align} \right]\times 2 $	2	28×28×128
残差层3	$\left[ \begin{align} & 3\times 3,256 \\ & 3\times 3,256 \\ \end{align} \right]\times 2 $	2	14×14×256
残差层4	$\left[ \begin{align} & 3\times 3,512 \\ & 3\times 3,512 \\ \end{align} \right]\times 2 $	2	7×7×512
Avgpool	7×7	512	1×1×512
全连接层	1×1	512	分类任务类别数

硬件/软件名称	规格/版本
CPU	16核Intel Xeon Gold 6430
GPU	NVIDIA RTX 4090（24 GB显存）
内存	120 GB
操作系统	Ubuntu 22.04
CUDA	12.4
Python	3.12
PyTorch	2.6.0
torchvision	0.21.0
NumPy	2.2.4
SciPy	1.15.2
Matplotlib	3.10.1
Scikit-learn	1.6.1
OpenCV	4.11.0.86

方法	$\frac{1}{2} $			$\frac{1}{3} $
方法	准确率	F1分数	召回率	准确率	F1分数	召回率
ARPL	96.90%	94.83%	96.77%	95.87%	94.83%	96.29%
MLS	96.50%	94.17%	96.35%	95.33%	94.17%	95.82%
网格搜索	99.75%	99.58%	99.75%	99.67%	99.58%	99.71%
本文方法	99.80%	99.80%	99.80%	99.73%	99.77%	99.77%
方法	$\frac{1}{6} $			$\frac{1}{11} $
方法	准确率	F1分数	召回率	准确率	F1分数	召回率
ARPL	94.83%	94.83%	95.03%	94.36%	94.83%	93.32%
MLS	94.17%	94.17%	94.43%	93.64%	94.17%	92.59%
网格搜索	99.58%	99.58%	99.58%	99.55%	99.58%	99.38%
本文方法	99.83%	99.83%	99.83%	99.82%	99.83%	99.75%