|本期目录/Table of Contents|

[1]赵自璐,王世练*,张炜,等.水下冲激噪声环境下基于多特征融合的信号调制方式识别[J].厦门大学学报(自然科学版),2017,56(03):416-422.[doi:10.6043/j.issn.0438-0479.201606011]
 ZHAO Zilu,WANG Shilian*,ZHANG Wei,et al.Classification of Signal Modulation Types Based on Multifeatures Fusion in Impulse Noise Underwater[J].Journal of Xiamen University(Natural Science),2017,56(03):416-422.[doi:10.6043/j.issn.0438-0479.201606011]
点击复制

水下冲激噪声环境下基于多特征融合的信号调制方式识别(PDF/HTML)
分享到:

《厦门大学学报(自然科学版)》[ISSN:0438-0479/CN:35-1070/N]

卷:
56卷
期数:
2017年03期
页码:
416-422
栏目:
研究论文
出版日期:
2017-05-24

文章信息/Info

Title:
Classification of Signal Modulation Types Based on Multifeatures Fusion in Impulse Noise Underwater
文章编号:
0438-0479(2017)03-0416-07
作者:
赵自璐王世练*张炜谢阳
国防科学技术大学电子科学与工程学院,湖南长沙410073
Author(s):
ZHAO ZiluWANG Shilian*ZHANG WeiXIE Yang
School of Electronic Science and Engineering,National University of Defense Technology,Changsha 410073,China
关键词:
冲激噪声调制识别Alpha稳定分布多特征
Keywords:
impulse noisemodulation classificationAlpha stable distributionmultifeatures
分类号:
TN 929.3
DOI:
10.6043/j.issn.0438-0479.201606011
文献标志码:
A
摘要:
通信信号调制方式的识别在水下通信系统中发挥着重要作用,但目前在传统理论基础上建立起的高斯白噪声环境下的识别方法在水下冲激噪声背景下的识别仍存在困难.针对这一问题提出了水下冲激噪声环境下多特征融合的调制方式识别方法.利用Alpha稳定分布建立水下冲激噪声的模型,提出了基于指数函数的非线性变换方法消除部分冲激噪声的信号预处理方法;对预处理后的信号提取频域的盒维数特征、信号包络的样本熵特征以及Stockwell变换(S变换)域能量熵特征,构成多特征向量进行融合识别.对3个种类和数量不同的调制信号集进行仿真实验,结果表明,多特征融合识别的方法在水下冲激噪声环境下较单一特征识别性能更好,对Alpha稳定分布的特征指数在1~2之间时,该方法具有稳定性.同时通过对比仿真发现,非线性变换预处理显著地提高了算法性能,且多特征融合的调制识别方法的性能明显优于单特征方法,可识别的信号种类更多.
Abstract:
Classification of signal modulation types plays an important role in underwater communication systems.However,present classical classification methods under white Gaussian noises exhibit poor performance under underwater impulse noises.This study proposes a method of modulation classification based on multifeatures fusion under underwater impulse noises.First,we apply the nonlinear transformation of the exponential function to eliminating part of impulse noises which are modeled by Alpha stable distribution model.Next,extract the signals′ frequency domain features of box dimension,envelopes features of sample entropy and Stockwell transform domain features of energy entropy,and then construct multifeatures vectors,which are given to support vector machine (SVM) for fusion and classification.Different signal sets are considered in MATLAB simulating experiments,which provide different number of signal schemes.Results show that the proposed method yields good performance and robustness under impulse noise.In comparison with the simulation,it is verified that the pretreatment of nonlinear transformation significantly improves the performance,and that the multifeatures method offers better performance than those single ones do,at the same time increases the identifiable signal types.

参考文献/References:

[1] 李世平,陈方超.基于小波和高阶累积的数字调制识别算法[J].计算机应用,2011,31(11):2926-2935.
[2] HO K C,PROKOPIW W,CHAN Y T.Modulation identification by the wavelet transform[C]∥Military Communications Conference.Piscataway:IEEE,1995:886-890.
[3] HO K C,PROKOPIW W,CHAN Y T.Modulation identification of digital signals by the wavelet transform[J].IEEE Proceedings:Radar,Sonar and Navigation,2000,147(4):169-176.
[4] LIU L,XU J.A novel modulation classification method based on high order cumulants[C]∥International Confe-rence on Wireless Communications,Networking and Mobile Computing.Wuhan:IEEE,2006:1-5.
[5] KETTERER H,JONDRAL F,COSTA A H.Classification of modulation modes using time-frequency methods[C]∥IEEE International Conference on Acoustics,Speech,Processing.Phoenix:IEEE,1999:2471-2474.
[6] SPOONER C M.On the utility of sixth-order cyclic cumulants for RF signal classification[C]∥The Thirty-Fifth Asilomar Conference on Signals,Systems and Computers.Pacific:IEEE,2001:890-897.
[7] SATIJA U,MOHANTY M,RAMKUMAR B.Automatic modulation classification using S-transform based features[C]∥International Conference on Signal Processing and Integrated Networks.Noida:IEEE,2015:708-712.
[8] 杨柳,赵晓群,徐静云.水声信号的调制方式识别[J].燕山大学学报,2014(2):156-162.
[9] 于志明.无线通信系统中的信号识别技术研究[D].哈尔滨:哈尔滨工程大学,2010:23-25.
[10] 刘明骞,李兵兵,石亚云.Alpha稳定分布噪声下数字调制识别新方法[J].西安电子科技大学学报(自然科学版),2015,42(6):1-5.
[11] 杨伟超.Alpha稳定分布噪声下通信信号调制识别研究[D].哈尔滨:哈尔滨工程大学,2012:13-30.
[12] SHAO M,NIKIAS C L.Detection and adaptive estimation of stable processes with fractional lower-order moments[C]∥IEEE Sixth SP Workshop on Statistical Signal and Array Processing.Victoria:IEEE,1992:94-97.
[13] TSIHRINTZIS G A,NIKIAS C L.Signal detection in incompletely characterized impulsive noise modeled as a stable process[C]∥Military Communications Conference.Piscataway:IEEE,1994:271-275.
[14] TSAKALIDES P,NIKIAS C L.Wideband array signal processing with alpha-stable distributions[C]∥Military Communications Conference.Piscataway:IEEE,1995:135-139.
[15] STOCKWELL R G,MANSINHA L,LOWE R P.Loca-lization of the complex spectrum:the S transform[J].IEEE Transactions on Signal Processing,1996,44(4):998-1001.
[16] 江伟华,曹秀岭,童峰,等.采用支持向量机的水声通信信号调制识别方法[J].厦门大学学报(自然科学版),2015,54(4):534-539.

备注/Memo

备注/Memo:
收稿日期:2016-06-09 录用日期:2016-11-10
*通信作者:wangsl@nudt.edu.cn
引文格式:赵自璐,王世练,张炜,等.水下冲激噪声环境下基于多特征融合的信号调制方式识别[J].厦门大学学报(自然科学版),2017,56(3):416-422.
Citation:ZHAO Z L,WANG S L,ZHANG W,et al.Classification of signal modulation types based on multi-features fusion in impulse noise underwater[J].J Xiamen Univ Nat Sci,2017,56(3):416-422.(in Chinese)
更新日期/Last Update: 1900-01-01