上海交通大学学报

上海交通大学学报 >

2024 , Vol. 58 >Issue 4: 481 - 491

DOI: https://doi.org/10.16183/j.cnki.jsjtu.2022.426

电子信息与电气工程

基于多流ConvNeXt网络和马氏距离度量的未知信号增量识别

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  • 1.哈尔滨工程大学 先进船舶通信与信息技术工业和信息化部重点实验室,哈尔滨 150000
    2.上海无线电设备研究所,上海 201100
肖易寒(1980-),博士,从事基于机器学习的雷达信号识别研究.

收稿日期: 2022-10-28

  修回日期: 2022-12-24

  录用日期: 2022-12-30

  网络出版日期: 2023-03-03

基金资助

国防科技基础加强计划(2019-JCJQ-ZD-067-00)

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Unknown Signal Incremental Recognition Based on Multi-Flow ConvNeXt Network and Mahalanobis Distance Metric

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  • 1. Key Laboratory of Advanced Marine Communication and Information Technology of the Ministry of Industry and Information Technology, Harbin Engineering University, Harbin 150000, China
    2. Shanghai Radio Equipment Research Institute, Shanghai 201100, China

Received date: 2022-10-28

  Revised date: 2022-12-24

  Accepted date: 2022-12-30

  Online published: 2023-03-03

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摘要

为解决现阶段基于深度学习网络的信号识别技术无法实现未知信号增量识别的问题,提出了基于多流ConvNeXt网络和马氏距离度量(MDM)相结合的未知信号增量识别方法.首先,利用改进的多流ConvNeXt网络提取信号的属性特征;其次,使用马氏距离度量判决方法进行未知信号检测进而实现已知信号和未知信号的二分类;最后,该方法根据不断增加的未知信号对模型的参数进行自动更新,使模型具备了自我进化的能力,进而可以识别出不断增加的新的未知信号类别,实现对未知信号的增量识别.仿真实验结果表明,该方法对未知信号的平均识别率达到97%以上.

关键词： 未知信号; 多流ConvNeXt网络; 马氏距离度量; 增量识别

本文引用格式

肖易寒, 刘序斌, 于祥祯, 赵忠凯 . 基于多流ConvNeXt网络和马氏距离度量的未知信号增量识别[J]. 上海交通大学学报, 2024 , 58(4) : 481 -491 . DOI: 10.16183/j.cnki.jsjtu.2022.426

Abstract

In order to solve the problem that the signal recognition technology based on deep learning network cannot currently realize the incremental recognition of unknown signals, a method for incremental recognition of such unknown signals, based on the combination of the multi-flow ConvNeXt network and Mahalanobis distance metric (MDM) is proposed. First, the improved multi-flow ConvNeXt network is used to extract the attribute features of signals. Then, the MDM judgement method is used to detect unknown signals, and apply the binary classification for known and unknown signals. Finally, the parameters of the model is automatically updated according to the increasing number of unknown signals. In such way, the model has the ability of self-evolution, and it has the ability to recognize incrementally more types of unknown signals.The simulation results show that the average recognition rate of unknown signals is more than 97%.

Key words： unknown signal; multi-flow ConvNeXt network; Mahalanobis distance metric; incremental recognition

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