通信学报 ›› 2017, Vol. 38 ›› Issue (12): 160-168.doi: 10.11959/j.issn.1000-436x.2017299

• 学术通信 • 上一篇    

基于ITD与纹理分析的特定辐射源识别方法

任东方,张涛,韩洁,王欢欢   

  1. 解放军信息工程大学信息系统工程学院,河南 郑州 450001
  • 修回日期:2017-09-06 出版日期:2017-12-01 发布日期:2018-01-19
  • 作者简介:任东方(1993-),男,河南平顶山人,解放军信息工程大学硕士生,主要研究方向为通信信号处理、辐射源识别等。|张涛(1977-),男,湖北天门人,解放军信息工程大学教授、博士生导师,主要研究方向为图像处理、辐射源识别,模式识别等。|韩洁(1990-),女,河南郑州人,解放军信息工程大学博士生,主要研究方向为通信信号处理、辐射源识别等。|王欢欢(1992-),男,河南永城人,解放军信息工程大学硕士生,主要研究方向为通信信号处理、辐射源识别等。
  • 基金资助:
    国家自然科学基金资助项目(61572518)

Specific emitter identification based on ITD and texture analysis

Dong-fang REN,Tao ZHANG,Jie HAN,Huan-huan WANG   

  1. Institute of Information System Engineering,Information Engineering University of PLA,Zhengzhou 450001,China
  • Revised:2017-09-06 Online:2017-12-01 Published:2018-01-19
  • Supported by:
    The National Natural Science Foundation of China(61572518)

摘要:

为解决基于希尔伯特黄变换(HHT,Hilbert-Huang transform)的特定辐射源识别方法在时频分析方面存在缺陷,所提特征可分性差的问题,该文基于固有时间尺度分解(ITD,intrinsic time-scale decomposition)提出一种新的辐射源个体识别方法。首先,通过固有时间尺度分解的方法将信号分解,进而得到其时频能量分布;之后,将信号时频能量谱转化为灰度图像,通过直方图统计和灰度共生矩阵提取图像纹理特征对不同信号进行识别。分别采用实测舰船通信信号以及仿真辐射源信号对所提算法进行性能测试,实验结果表明,其性能优于2种基于希尔伯特黄变换的方法。

关键词: 特定辐射源识别, 固有时间尺度分解, 时频能量分布, 纹理分析

Abstract:

To solve the defects of time-frequency analysis and poor separability of extracted features in specific emitter identification (SEI) based on Hilbert-Huang transform (HHT),a novel SEI method based on intrinsic time-scale decomposition(ITD)was proposed.ITD was utilized to decompose the emitter signals and get the time-frequency energy distribution(TFED)firstly,later the TFED spectrum was transformed into gray image and several image texture features through histogram statistic and gray-level co-occurrence matrix was extracted for identification.Measured ship communication signals and simulated emitter signals were used to test the performance of proposed method.Compared with another two SEI methods based on HHT,the proposed method is proved more effective in identification accuracy.

Key words: SEI, ITD, TFED spectrum, texture analysis

中图分类号: 

  • TN911.7