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半监督条件下基于朴素贝叶斯和Choi-Williams时频分布能量积累的雷达信号识别

王红卫 董鹏宇 陈游 周一鹏 肖冰松

王红卫, 董鹏宇, 陈游, 周一鹏, 肖冰松. 半监督条件下基于朴素贝叶斯和Choi-Williams时频分布能量积累的雷达信号识别[J]. 电子与信息学报, 2021, 43(3): 589-597. doi: 10.11999/JEIT200127
引用本文: 王红卫, 董鹏宇, 陈游, 周一鹏, 肖冰松. 半监督条件下基于朴素贝叶斯和Choi-Williams时频分布能量积累的雷达信号识别[J]. 电子与信息学报, 2021, 43(3): 589-597. doi: 10.11999/JEIT200127
Hongwei WANG, Pengyu DONG, You CHEN, Yipeng ZHOU, Bingsong XIAO. Recognition Method of Radar Signal Based on the Energy Cumulant of Choi-Williams Distribution and Improved Semi-supervised Naïve Bayes[J]. Journal of Electronics and Information Technology, 2021, 43(3): 589-597. doi: 10.11999/JEIT200127
Citation: Hongwei WANG, Pengyu DONG, You CHEN, Yipeng ZHOU, Bingsong XIAO. Recognition Method of Radar Signal Based on the Energy Cumulant of Choi-Williams Distribution and Improved Semi-supervised Naïve Bayes[J]. Journal of Electronics and Information Technology, 2021, 43(3): 589-597. doi: 10.11999/JEIT200127

半监督条件下基于朴素贝叶斯和Choi-Williams时频分布能量积累的雷达信号识别

doi: 10.11999/JEIT200127
基金项目: 航空科学基金(20175596020)
详细信息
    作者简介:

    王红卫:男,1974年生,副教授,博士,研究方向为信息对抗理论与技术,电子对抗总体技术

    董鹏宇:男,1995年生,硕士生,研究方向为信息对抗理论与技术,雷达信号处理

    陈游:男,1983年生,副教授,博士后,研究方向为信息对抗理论与技术

    周一鹏:男,1992年生,博士生,研究方向雷达信号处理,电子对抗技术

    肖冰松:男,1982年生,副教授,研究方向为雷达信号处理

    通讯作者:

    董鹏宇 Hickey1212@163.com

  • 中图分类号: TN97

Recognition Method of Radar Signal Based on the Energy Cumulant of Choi-Williams Distribution and Improved Semi-supervised Naïve Bayes

Funds: Aeronautical Science Foundation (20175596020)
  • 摘要: 针对非合作电子侦察雷达信号识别中先验信息残缺的问题,该文提出一种基于Choi-Williams时频分布(CWD)的改进半监督朴素贝叶斯的识别算法(ISNB)。首先对CWD进行降噪预处理,然后通过计算降噪后CWD不同时间下各频率采样值的积累量,从而得到CWD的能量积累量这一新特征;针对传统的半监督朴素贝叶斯(SNB)在更新训练样本集过程中会产生迭代错误的不足,通过在无标签样本集生成的置信度列表中选取置信度高的样本添加到有标签样本集中,再利用预测后的分类结果对分类器参数进行改进,进而构建改进的SNB分类器,有效解决了传统SNB算法分类精度低且分类性能不稳定的缺点。理论分析和仿真结果表明,所提方法相比于传统SNB算法均提高了3%左右;在相同信噪比下,相比于传统的主成分分析加支持向量机法,该算法具有更高的分类识别率和更好的分类性能。
  • 图  1  6种调制信号在信噪比为10 dB时的CWD时频分布图

    图  2  6种调制信号在信噪比为–5 dB时的CWD时频分布图

    图  3  6种调制信号在信噪比为10 dB时的CWD时频能量积累图

    图  4  6种调制信号在信噪比为–5 dB时的CWD时频能量积累图

    图  5  NLFM信号降噪前后的CWD, EC-CWD及振幅直方图

    图  6  辐射源信号识别流程

    图  7  两种算法平均识别率对比

    表  1  两种方法在不同标记样本下的识别效果比较

    有标签样本比例(%)510152025
    不同算法SNBISNBSNBISNBSNBISNBSNBISNBSNBISNB
    LFM86.2589.0089.5093.7592.2596.2595.7598.5099.0099.75
    NLFM87.7589.5088.5090.2591.2591.0090.5092.5090.5092.50
    SFM79.2581.7582.5085.0085.2587.0086.7589.5091.0093.75
    Frank76.0080.2578.2583.0081.7583.2585.5085.2587.2589.50
    QPSK85.7588.2586.2589.7588.0090.5088.7591.2589.7592.00
    PSK/FSK77.5081.5079.0083.2583.7585.2584.5086.7588.2592.50
    平均识别率(%)82.0885.0484.0086.8387.0488.8888.6390.6490.9693.33
    下载: 导出CSV

    表  2  不同信噪比下的识别正确率

    信噪比(dB)–202468
    文献[19]+SNB51.3558.2665.3373.7977.5581.86
    PCA-SVM38.6745.3358.9167.6875.7281.93
    SNB55.2861.2868.7476.5684.5790.96
    ISNB58.3767.3772.5877.4287.4493.33
    下载: 导出CSV
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出版历程
  • 收稿日期:  2020-02-26
  • 修回日期:  2020-09-30
  • 网络出版日期:  2020-10-12
  • 刊出日期:  2021-03-22

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