Journal of Shanghai Jiao Tong University

Journal of Shanghai Jiaotong University >

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

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

Electronic Information and Electrical Engineering

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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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

Cite this article

XIAO Yihan, LIU Xubin, YU Xiangzhen, ZHAO Zhongkai . Unknown Signal Incremental Recognition Based on Multi-Flow ConvNeXt Network and Mahalanobis Distance Metric[J]. Journal of Shanghai Jiaotong University, 2024 , 58(4) : 481 -491 . DOI: 10.16183/j.cnki.jsjtu.2022.426

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