北京航空航天大学学报 ›› 2017, Vol. 43 ›› Issue (8): 1684-1692.doi: 10.13700/j.bh.1001-5965.2016.0576

• 论文 • 上一篇    下一篇

基于改进HHT的非高斯噪声中瞬态通信信号检测

郭晓陶, 王星, 周东青, 张莹   

  1. 空军工程大学航空航天工程学院, 西安 710038
  • 收稿日期:2016-07-05 修回日期:2016-10-01 出版日期:2017-08-20 发布日期:2016-12-13
  • 通讯作者: 王星 E-mail:wx1965@sohu.com
  • 作者简介:郭晓陶,男,硕士研究生。主要研究方向:通信辐射源个体识别、信息融合;王星,男,博士,教授,博士生导师。主要研究方向:电子对抗理论与技术。
  • 基金资助:
    航空科学基金(20152096019)

Transient communication signal detection under non-Gaussian noise based on improved HHT

GUO Xiaotao, WANG Xing, ZHOU Dongqing, ZHANG Ying   

  1. Aeronautics and Astronautics Engineering College, Air Force Engineering University, Xi'an 710038, China
  • Received:2016-07-05 Revised:2016-10-01 Online:2017-08-20 Published:2016-12-13
  • Supported by:
    Aeronautical Science Foundation of China (20152096019)

摘要: 根据瞬态通信信号和非高斯噪声的特点,建立了相应的信号模型,并利用希尔伯特-黄变换(HHT)处理非线性非平稳信号的优势,提出了基于改进HHT的非高斯噪声中瞬态通信信号的检测算法。该检测算法分为集合经验模式分解(EEMD)和固有模态函数(IMF)分量筛选两部分,首先经过加入随机白噪声多次试验取均值得到待检测信号的IMF分量,再结合各个分量与原信号的能量差异和相关性剔除虚假IMF分量,从而实现对混叠在非高斯噪声中的瞬态通信信号的有效检测。仿真在不同的条件下对比了本文算法与其他算法对信号的检测效果,结果证明本文算法能够有效克服HHT中存在的缺陷,实现对瞬态信号更为准确的分析和检测。

关键词: 改进HHT, 集合经验模式分解(EEMD), 瞬态通信信号, 非高斯噪声, 信号检测

Abstract: Based on the characteristics of transient communication signals and non-Gaussian noise, the corresponding signal models were established, and a novel algorithm of transient communication signal detection under non-Gaussian noise was proposed based on the improved Hilbert-Huang transform (HHT), which takes the advantage of HHT in processing non-linear and non-stationary signals. The improved detection algorithm was divided into two sections:ensemble empirical mode decomposition (EEMD) and filter of intrinsic mode function (IMF) components. First, signals were decomposed into several IMF components by adding random white noise and averaging, and then false components were eliminated by energy variance and correlation, through which transient signals aliased in the non-Gaussian noise can be detected effectively. Simulation compares the detection efficiency of HHT and the proposed algorithm, and the results demonstrate that the proposed algorithm can reduce the influence of model mixing and false frequency caused by HHT and achieve more accurate analysis to the time-frequency characteristics of transient communication signal.

Key words: improved HHT, ensemble empirical mode decomposition (EEMD), transient communication signal, non-Gaussian noise, signal detection

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