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

doi:10.13700/j.bh.1001-5965.2016.0576

基于改进HHT的非高斯噪声中瞬态通信信号检测

doi: 10.13700/j.bh.1001-5965.2016.0576

空军工程大学航空航天工程学院, 西安 710038

基金项目:

航空科学基金 20152096019

详细信息

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

通讯作者:
王星, E-mail: wx1965@sohu.com

中图分类号: TN911.4;TN911.2
计量
- 文章访问数: 112
- HTML全文浏览量: 26
- PDF下载量: 3
- 被引次数: 0
出版历程
- 收稿日期: 2016-07-05
- 录用日期: 2016-10-01
- 网络出版日期: 2017-08-20
- 整期出版日期: 2017-08-20

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

Aeronautics and Astronautics Engineering College, Air Force Engineering University, Xi'an 710038, China

Funds:

Aeronautical Science Foundation of China 20152096019

More Information

Corresponding author: WANG Xing, E-mail: wx1965@sohu.com

摘要

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。
- 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.
- improved HHT /
- ensemble empirical mode decomposition (EEMD) /
- transient communication signal /
- non-Gaussian noise /
- signal detection

HTML全文

图 1 HHT算法流程图

Figure 1. Flowchart of HHT algorithm

下载: 全尺寸图片幻灯片

图 2 模态混叠示意图

Figure 2. Sketch map of mode mixing

下载: 全尺寸图片幻灯片

图 3 虚假频率示意图

Figure 3. Sketch map of illusive frequency

下载: 全尺寸图片幻灯片

图 4 瞬态通信信号波形

Figure 4. Waveform of transient communication signal

下载: 全尺寸图片幻灯片

图 5 非高斯噪声波形

Figure 5. Waveform of non-Gaussian noise

下载: 全尺寸图片幻灯片

图 6 通信信号及其组成波形

Figure 6. Waveform of communication signal and its components

下载: 全尺寸图片幻灯片

图 7 EMD分解结果

Figure 7. Decomposition results of EMD

下载: 全尺寸图片幻灯片

图 8 HHT能量时频分布

Figure 8. Energy time-frequency distribution of HHT

下载: 全尺寸图片幻灯片

图 9 EEMD分解结果

Figure 9. Decomposition results of EEMD

下载: 全尺寸图片幻灯片

图 10 改进HHT能量时频分布

Figure 10. Energy time-frequency distribution of improved HHT

下载: 全尺寸图片幻灯片

表 1 3种算法的正确检测率

Table 1. Correct detection rates of three algorithms%

%

检测算法信噪比/dB

10 0 -10

SE 90.13 86.94 81.27

HHT 92.67 89.71 86.83

IHHT 93.75 91.62 90.27

下载: 导出CSV

参考文献(15)

[1]	KLEN R W, TEMPLE M A, MENOONHALL M J.Application of wavelet denoising to improve OFDM-based signal detection and classification[J].Security and Communication Networks, 2010, 3(1):71-82.
[2]	CARROLL T L.A nonlinear dynamics method for signal identification[J].Chaos, 2007, 17(2):023109-1-023109-7. doi: 10.1063/1.2722870
[3]	WACHOWSKI N, AZIMI-SADJADI M R.Detection and classification of nonstationary transient signals using sparse approximations and Bayesian networks[J].IEEE/ACM Transactions on Speech and Language Processing, 2014, 22(12):1750-1764. doi: 10.1109/TASLP.2014.2348913
[4]	王燕, 邹男, 付进, 等.基于局部瞬时能量密度级的瞬态信号检测方法[J].电子与信息学报, 2013, 35(7):1720-1724. http://www.cnki.com.cn/Article/CJFDTOTAL-DZYX201307032.htm WANG Y, ZOU N, FU J, et al.Transient signal detection method based on partial instantaneous energy density level[J].Journal of Electronics & Information Technology, 2013, 35(7):1720-1724(in Chinese). http://www.cnki.com.cn/Article/CJFDTOTAL-DZYX201307032.htm
[5]	HUANG N E, WU Z H.A review on Hilbert-Huang transform:Method and its application to geophysical studies[J].Reviews of Geophysics, 2008, 46(6):1029-1039. doi: 10.1029/2007RG000228/full?isReportingDone=true
[6]	HUANG N E, SHEN S S P.The Hilbert-Huang transform and its application[M].Kuala Lumpur:World Scientific Publishing Co.Pte.Ltd, 2005:142-146.
[7]	DIVIN Y, LYATTI M, SNEZHKO A.THz Hilbert-transform spectrum analyzer based on high-T_c Josephson junction in stirling cryocooler[J].IEEE Transactions on Applied Superconductivity, 2013, 23(3):4-7. http://ieeexplore.ieee.org/document/6365238/
[8]	杨振, 邹男, 付进.Hilbert-Huang变换在瞬态信号检测中的应用[J].声学技术, 2015, 34(2):167-171. http://www.cnki.com.cn/Article/CJFDTOTAL-SXJS201502016.htm YANG Z, ZOU N, FU J.The application of Hilbert-Huang transform in transient signal detection[J].Technical Acoustics, 2015, 34(2):167-171(in Chinese). http://www.cnki.com.cn/Article/CJFDTOTAL-SXJS201502016.htm
[9]	李明爱, 崔燕, 杨金福, 等.基于HHT和CSSD的多域融合自适应脑电特征提取方法[J].电子学报, 2013, 41(12):2479-2486. doi: 10.3969/j.issn.0372-2112.2013.12.025 LI M A, CUI Y, YANG J F, et al.An adaptive multi-domain fusion feature extraction with method HHT and CSSD[J].Acta Electronicasinica, 2013, 41(12):2479-2486(in Chinese). doi: 10.3969/j.issn.0372-2112.2013.12.025
[10]	程擂, 韩焱, 王鉴, 等.基于改进HHT的水中爆炸冲击波信号时频特性分析方法[J].爆炸与冲击, 2011, 31(3):326-331. http://www.cnki.com.cn/Article/CJFDTOTAL-BZCJ201103020.htm CHENG L, HAN Y, WANG J, et al Time-frequency representation analysis in underwater explosive shock wave signals based on an improved HHT method[J].Explosion and Shock Waves, 2011, 31(3):326-331(in Chinese). http://www.cnki.com.cn/Article/CJFDTOTAL-BZCJ201103020.htm
[11]	江向东, 杨德森, 时胜国.基于高阶统计量的小波包去噪在瞬态信号检测中的应用研究[J].信号处理, 2005, 21(2):126-130. http://www.cnki.com.cn/Article/CJFDTOTAL-XXCN200502004.htm JIANG X D, YANG D S, SHI S G.Wavelet packets de-noising based on high-order-statistic for transient detection[J].Signal Processing, 2005, 21(2):126-130(in Chinese). http://www.cnki.com.cn/Article/CJFDTOTAL-XXCN200502004.htm
[12]	窦东阳, 赵英凯.集合经验模式分解在旋转机械故障诊断中的应用[J].农业工程学报, 2010, 26(2):190-196. http://www.cnki.com.cn/Article/CJFDTOTAL-NYGU201002034.htm DOU D Y, ZHAO Y K.Application of ensemble empirical mode decomposition in failure analysis of rotating machinery[J].Transactions of the CSAE, 2010, 26(2):190-196(in Chinese). http://www.cnki.com.cn/Article/CJFDTOTAL-NYGU201002034.htm
[13]	谢静, 谭佐军, 陈阳, 等.Hilbert-Huang变换分析THz脉冲信号的时频特性[J].强激光与粒子束, 2014, 26(7):131-135. http://www.cnki.com.cn/Article/CJFDTOTAL-QJGY201407028.htm XIE J, TAN Z J, CHEN Y, et al.THz time-frequency analysis with Hilbert-Huang transform[J].High Power Laser and Particle Beams, 2014, 26(7):131-135(in Chinese). http://www.cnki.com.cn/Article/CJFDTOTAL-QJGY201407028.htm
[14]	韩明, 刘教民, 孟军英, 等.一种自适应调整K-ρ的混合高斯背景建模和目标检测算法[J].电子与信息学报, 2014, 36(8):2023-2027. http://www.cnki.com.cn/Article/CJFDTOTAL-DZYX201408040.htm HAN M, LIU J M, MENG J Y, et al.A modeling and target detection algorithm based on adaptive adjustment K-ρ for mixture Gaussian background[J].Journal of Electronics & Information Technology, 2014, 36(8):2023-2027(in Chinese). http://www.cnki.com.cn/Article/CJFDTOTAL-DZYX201408040.htm
[15]	李荣荣, 胡昌奎, 余娟.基于谱熵的语音端点检测算法改进研究[J].武汉理工大学学报, 2013, 35(7):134-139. http://www.cnki.com.cn/Article/CJFDTOTAL-WHGY201307027.htm LI R R, HU C K, YU J.Research of speech endpoint detection based on spectral entropy algorithm[J].Journal of Wuhan University of Technology, 2013, 35(7):134-139(in Chinese). http://www.cnki.com.cn/Article/CJFDTOTAL-WHGY201307027.htm