Specific Emitter Identification Under Open Set Scenes

WANG Chunsheng; WANG Yongmin; XU Hua; ZHANG Yue; ZHU Lili

doi:10.7652/xjtuxb202210013

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Specific Emitter Identification Under Open Set Scenes

更新时间：2025-07-09

- Specific Emitter Identification Under Open Set Scenes
- Vol. 56, Issue 10, Pages: 130-140(2022)
- 作者机构：
  
  空军工程大学信息与导航学院,西安,710077
- 作者简介：
- 基金信息：
- DOI：10.7652/xjtuxb202210013
  CLC： TN975
- Online First：10 October 2022，
  
  Published：2022
- 稿件说明：
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WANG Chunsheng, WANG Yongmin, XU Hua, et al. Specific Emitter Identification Under Open Set Scenes[J]. 2022, 56(10): 130-140.
DOI：

WANG Chunsheng, WANG Yongmin, XU Hua, et al. Specific Emitter Identification Under Open Set Scenes[J]. 2022, 56(10): 130-140. DOI： 10.7652/xjtuxb202210013.

摘要

为解决现有开集场景下辐射源个体识别算法鲁棒性不强、识别性能较低的问题

结合原型网络提出了一种开集场景下适用于一维信号的辐射源个体识别模型。对采集的辐射源I/Q信号进行功率归一化、切片和加噪等预处理操作; 设计用于识别I/Q信号的一维卷积神经网络

并在网络中加入具有注意力机制的压缩激励模块

以此提高网络中有效特征通道的权重; 将该网络结构和原型学习思想相结合

利用预处理的数据集进行训练和测试。在训练过程中

利用距离交叉熵损失函数为每个类别学习一个原型

并将信号特征到原型的距离作为分类依据

同时利用原型损失函数以提高信号类内紧密度的方式增大类间距离

进一步增强分类能力; 在测试过程中

利用自适应距离分类规则为每个类别学习一个自适应的距离阈值

并通过距离阈值完成对已知目标的分类和未知目标的检测。对5种ZigBee设备采集的信号进行实验

结果表明:在信噪比为-10～10 dB之间

所提模型的识别准确率比通常所用的基于极值理论的模型高8%左右。

Abstract

This paper proposes a specific emitter identification model suitable for one-dimensional signals under open set scenes based on prototype network. The model is used to solve the problems of weak robustness and low recognition of existing algorithms for specific emitter identification under open set scenes. Firstly

the I/Q signals collected are preprocessed by power normalization

slicing and noise addition. Then

a one-dimensional convolutional neural network for recognition of I/Q signals is designed

and a squeeze-and-excitation block with attentional mechanism is added to the network to improve the weights of feature channels with high efficiency. Finally

the preprocessed data set is used to train and test the network based on the idea of prototype learning. During the training process

the distance cross entropy loss function is used for prototype learning for each category

taking the distance between the signal features and the prototype as the classification basis. At the same time

the prototype loss function is used to increase the inter-class distance by compacting the intra-class signals

which further enhances the classification ability. During the testing process

the network can learn an adaptive distance threshold for each category by using the adaptive distance classification rule. The distance threshold is used for the classification of known classes and the detection of unknown ones. Experiments are carried out on the signals collected from five ZigBee devices. Simulation results show that the recognition rate of proposed model is about 8% higher than the common models based on extreme value theory between a signal-to-noise ratio from -10 dB to 10 dB.

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references

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