采用主分量分析的非合作水声通信信号调制识别

doi:10.3969/j.issn.1000-1093.2016.09.017

兵工学报 ›› 2016, Vol. 37 ›› Issue (9): 1670-1676.doi: 10.3969/j.issn.1000-1093.2016.09.017

采用主分量分析的非合作水声通信信号调制识别

江伟华¹，童峰¹，王彬²，刘世刚²

(1.厦门大学水声通信与海洋信息技术教育部重点实验室，福建厦门 361005；2.解放军信息工程大学，河南郑州 450002）

收稿日期:2015-11-01 修回日期:2015-11-01 上线日期:2016-11-04
通讯作者: 江伟华 E-mail:whjiang@stu.xmu.edu.cn
作者简介:江伟华(1991—)，男，博士研究生
基金资助:
国家自然科学基金项目（11274259、11574258）

Modulation Recognition Method of Non-cooperation Underwater Acoustic Communication Signals Using Principal ComponentAnalysis

JIANG Wei-hua¹, TONG Feng¹, WANG Bin², LIU Shi-gang²

(1.Key Laboratory of Underwater Acoustic Communication and Marine Information Technology of Ministry of Education,Xiamen University, Xiamen 361005, Fujian, China;2. The PLA Information Engineering University, Zhengzhou 450002, Henan,China)

Received:2015-11-01 Revised:2015-11-01 Online:2016-11-04
Contact: JIANG Wei-hua E-mail:whjiang@stu.xmu.edu.cn

摘要/Abstract

摘要： 由于信道传输特性、信噪比低等因素的影响，非合作水声通信信号的调制识别极具挑战性。对信号功率谱、平方谱进行主分量分析，提取代表不同类型调制信号特有信息的主分量作为特征参数，从而降低特征参数维度、抑制噪声影响，并在此基础上设计一种基于人工神经网络的水声通信信号调制方式分类器。海上实录信号数据的识别实验结果表明了该方法的有效性。

关键词: 声学, 水声通信信号, 谱特征, 调制识别, 主分量分析, 人工神经网络分类器

Abstract: The modulation classification of the non-cooperation underwater acoustic communication signals is extremely challenging due to channel transmission characteristics and low signal-to-noise ratio. The principal component analysis (PCA) is used to analyze the power spectra and square spectrum features of signals, which is capable of extracting the principal components associated with different modulated signals as input vector, thus reducing the feature dimension and suppressing the influence of noise. An artificial neural network (ANN) classifier is proposed for modulation recognition. The experimental modulation classification results obtained from field signals in 4 different underwater acoustic channels show that the proposed modulation recognition method has good classification performance.

Key words: acoustics, underwater acoustic digital modulated signal, spectrum feature, modulation recognition, principal component analysis, ANN classifier

中图分类号:

TN929.3

江伟华，童峰，王彬，刘世刚. 采用主分量分析的非合作水声通信信号调制识别[J]. 兵工学报, 2016, 37(9): 1670-1676.

JIANG Wei-hua, TONG Feng, WANG Bin, LIU Shi-gang. Modulation Recognition Method of Non-cooperation Underwater Acoustic Communication Signals Using Principal ComponentAnalysis[J]. Acta Armamentarii, 2016, 37(9): 1670-1676.

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采用主分量分析的非合作水声通信信号调制识别

Modulation Recognition Method of Non-cooperation Underwater Acoustic Communication Signals Using Principal ComponentAnalysis

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