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基于谱图分解的宽带通信信号智能检测算法

易冬 马瑞鹏 胡涛 成凯鑫 吴迪 田志富 王艳云

易冬, 马瑞鹏, 胡涛, 等. 基于谱图分解的宽带通信信号智能检测算法[J]. 强激光与粒子束, 2023, 35: 069001. doi: 10.11884/HPLPB202335.230024
引用本文: 易冬, 马瑞鹏, 胡涛, 等. 基于谱图分解的宽带通信信号智能检测算法[J]. 强激光与粒子束, 2023, 35: 069001. doi: 10.11884/HPLPB202335.230024
Yi Dong, Ma Ruipeng, Hu Tao, et al. Intelligent detection algorithm of broadband communication signal based on spectral decomposition[J]. High Power Laser and Particle Beams, 2023, 35: 069001. doi: 10.11884/HPLPB202335.230024
Citation: Yi Dong, Ma Ruipeng, Hu Tao, et al. Intelligent detection algorithm of broadband communication signal based on spectral decomposition[J]. High Power Laser and Particle Beams, 2023, 35: 069001. doi: 10.11884/HPLPB202335.230024

基于谱图分解的宽带通信信号智能检测算法

doi: 10.11884/HPLPB202335.230024
详细信息
    作者简介:

    易 冬,yidong2021@sina.cn

    通讯作者:

    马瑞鹏,13164351610@163.com

  • 中图分类号: TN911

Intelligent detection algorithm of broadband communication signal based on spectral decomposition

  • 摘要: 对于宽带通信信号检测问题,针对目前基于深度学习的信号检测算法不适应于处理大带宽和大时宽的宽带信号以及对信号时频参数估计存在的固有偏差问题,提出基于谱图分解的宽带通信信号智能检测算法,完成对大带宽接收信号中窄带信号的高效准确检测。首先将由宽带信号转化而来的灰度时频谱图通过谱图分解得到适合于目标检测网络输入大小的子谱图,然后使用改进的无锚框YOLOx目标检测算法对子谱图中的窄带信号进行检测,最后将子谱图的信号检测结果融合得到窄带信号的时频参数等检测结果。经过实验测试得出,该算法能够适应复杂的噪声环境,与其他深度学习算法和传统算法相比,具有较高的信号检测概率,较低的虚警概率,较小的信号参数估计平均误差,其检测精度更高,鲁棒性、实用性、通用性更强。
  • 图  1  信号时频谱图

    Figure  1.  Signal time spectrum diagram

    图  2  基于谱图分解的大带宽时宽通信信号智能检测算法结构

    Figure  2.  Structure of intelligent detection algorithm for large bandwidth time-width communication signal based on spectral decomposition

    图  3  原图直接输入目标检测网络

    Figure  3.  Original image is directly fed into the target detection network

    图  4  原图经分解后输入目标检测网络

    Figure  4.  Original image is decomposed and input into the target detection network

    图  5  窄带信号与谱图分解窗口的相对分布情况

    Figure  5.  Relative distribution of narrowband signal and spectral decomposition window

    图  6  YOLOx算法检测流程

    Figure  6.  YOLOx algorithm detection process

    图  7  IOU相同的边框交叠情况对比

    Figure  7.  The same IOU border overlapping situation contrast

    图  8  预测框融合过程示意图

    Figure  8.  Diagram of prediction box fusion process

    图  9  各检测算法性能对比

    Figure  9.  Performance comparison of detection algorithms

    图  10  窄带信号是否预分类下的智能检测算法性能对比

    Figure  10.  Performance comparison of intelligent detection algorithms under preclassification of signal targets

    图  11  采用CIOU损失与IOU损失下的本文算法检测性能对比

    Figure  11.  Comparison of detection performance of the proposed algorithm under CIOU loss and IOU loss

    表  1  仿真数据集主要参数

    Table  1.   Results of experiments

    parametervaluesparametervalues
    bandwidth range 0~3200 kHz signal types wideband real signal
    sampling frequency 6400 kHz time range 2000 ms
    time-frequency spectrum frequency resolution 1 kHz time-frequency spectrum time resolution 1 ms
    size of time-frequency spectrum 3200×3999 the number of narrowband signals in a wideband signal 40~70
    target signal frequency range 5~3115 kHz target signal bandwidth range 5~100 kHz
    target signal modulation pattern 2FSK, 4FSK, 8FSK, MPSK whether there is a burst signal yes
    burst signal burst interval 10~1500 ms burst signal duration 10~1500 ms
    parameter values
    channel environment Rayleigh fading channel + non-stationary undulation noise + α-stabilized noise
    number of target signals in training set 800 time-frequency spectrums, a total of 40 854 signals, 75% for training, 25% for validation
    number of target signals in test set 0~20 dB stepped at 2 dB, and 100 time-frequency spectrums under each SNR, with a total of
    61 373 signals
    下载: 导出CSV

    表  2  检测网络对单子谱图的检测时间

    Table  2.   Detection time of monad spectra detected by the network

    detection algorithmsingle sub-spectrum detection time/ms
    proposed algorithm45.0
    spectrogram decomposition combined with Ref. [16]50.1
    下载: 导出CSV
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出版历程
  • 收稿日期:  2023-01-11
  • 修回日期:  2023-03-02
  • 录用日期:  2023-03-02
  • 网络出版日期:  2023-03-08
  • 刊出日期:  2023-05-06

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