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

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.

参考文献

［1］ Wu Z, Yang T C, Liu Z, et al. Modulation detection of underwater acoustic communication signals through cyclostationary analysis[C]∥2012 Military Communications Conference. Orlando, FL:IEEE, 2012.
[2］ Li X, Han Q, Liu Z, et al. Novel modulation detection scheme for underwater acoustic communication signal through short-time detailed cyclostationary features[C]∥2014 IEEE Wireless Communications and Networking Conference. Xi'an, Shaanxi:IEEE, 2014:624-629.
[3］ Nandi A K, Azzouz E E. Automatic analogue modulation recognition[J]. Signal Processing, 1995, 46(2):211-222.
[4］ Nandi A K, Azzouz E E. Algorithms for automatic modulation recognition of communication signals[J]. IEEE Transactions on Communications, 1998, 46(4):431-436.
[5］ Swami A, Sadler B M. Hierarchical digital modulation classification using cumulates[J]. IEEE Transactions on Communications, 2000, 48(3):416-429.
[6］ Gardner W A, Spooner C M. Cyclic-spectral analysis for signal detection and modulation recognition[C]∥1988 Military Communications Conference. San Diego, CA:IEEE, 1988:419-424.
[7］范海波,杨志俊,曹志刚.卫星通信常用调制方式的自动识别[J].通信学报, 2004, 25(1):140-149.
FAN Hai-bo, YANG Zhi-jun, CAO Zhi-gang. Automatic recognition for com2mon used modulations in satellite communication[J]. Journal of China Institute of Communications, 2004, 25(1):140-149. (in Chinese)
[8］刘明骞, 李兵兵, 赵雷. 数字调制信号识别的特征参数优化方法[J].计算机科学, 2011, 38(11):79-82.
LIU Ming-qian, LI Bing-bing, ZHAO Lei. Feature optimization for digital modulation signals recognition[J]. Computer Science, 2011, 38(11):79-82.(in Chinese)
[9］董建超，杨铁军，李新辉, 等. 主分量分析在激励源识别中的应用研究[J]. 振动与冲击，2013,32(24):157-163.
DONG Jian-chao, YANG Tie-jun, LI Xin-hui, et al. Applications of principal component analysis in excitation source identification[J]. Journal of Vibration and Shock, 2013, 32(24):157-163.(in Chinese)
[10］ Tong F, Tso S K, Hung M Y Y. Impact-acoustics-based health monitoring of tile-wall bonding integrity using principal component analysis[J]. Journal of Sound and Vibration, 2006, 294(S1/S2): 329-340.
[11］ Liu H, Yu L. Toward integrating feature selection algorithms for classification and clustering[J]. IEEE Transactions on Knowledge and Data Engineering, 2005, 17(3):491-502.
[12］ Soares-Filho W, de Seixas J M, Caloba L P. Principal component analysis for classifying passive sonar signals[C]∥The 2001 IEEE International Symposium on Circuits and System. Sydney, NSW:IEEE, 2001:592-595.
[13］魏鑫, 李钢虎, 王志强. 基于小波包与PCA方法对水下目标识别研究[J]. 计算机仿真, 2011, 28(8):8-11.
WEI Xin, LI Gang-hu, WANG Zhi-qiang. Underwater target recognition based on wavelet packet and principal component analysis[J]. Computer Simulation, 2011, 28(8):8-11.(in Chinese)
[14］洪先成, 张国毅, 李冬梅, 等. 多特征融合的雷达信号脉内调制识别[J]. 信息与电子工程, 2011, 9(5):551-555.
HONG Xian-cheng, ZHANG Guo-yi, LI Dong-mei, et al. Fusion of characters for intra-pulse modulation recognition of radar signal[J]. Information and Electronic Engineering, 2011, 9(5):551- 555.(in Chinese)
[15］常威威，郭雷，刘坤,等.基于主分量分析的高光谱遥感数据噪声消除方法[J].计算机测量与控, 2009,17(6):1070-1072.
CHANG Wei-wei, GUO Lei, LIU Kun, et al. Denoising of hyperspectral data based on wavelet transform and principal component analysis[J]. Computer Measurement & Control, 2009, 17(6): 1070-1072.(in Chinese)
[16］ Haykin S O. Neural networks and learning machine[M]. Upper Saddle River, New Jersey : Prentice Hall, 2008.
[17］ Ripley B D. Pattern recognition and neural networks[M]. Cambridge:Cambridge University Press, 2008.
[18］梁传建，杨国来，王晓锋. 基于神经网络和遗传算法的火炮结构动力学优化[J]. 兵工学报，2015, 36(5):789-794.
LIANG Chuan-jian， YANG Guo-lai， WANG Xiao-feng. Structural dynamics optimization of gun based on neural networks and genetic algorithms[J]. Acta Armamentarii, 2015, 36(5):789-794.(in Chinese)
[19］ Chan Y T, Gadboi L G. Identification of the modulation type of a signal[J]. Signal Processing, 1989,16(2):149-154.
[20］江伟华，曹秀岭，童峰. 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.
JIANG Wei-hua, CAO Xiu-ling, TONG Feng. Modulation recognition method of underwater acoustic communication signals using SVM [J]. Journal of Xiamen University:Natural Science, 2015,54(4):534-539. (in Chinese)
[21］黄如容.数字信号调制方式识别方法的设计与实现[D]. 成都:西南交通大学, 2012:8-10.
HUANG Ru-rong. Design and realization of a method of automatic modulation recognition of digital signals[D]. Chengdu:Southwest Jiaotong University, 2012:8-10.(in Chinese)
[22］刘芳.基带通信信号的参数估计-调制识别及解调[D]. 济南:山东大学, 2007:40.
LIU Fang. Parameter estimation, modulation recognition and demodulation of communication signals on baseband[D]. Jinan:Shandong University, 2007:40.(in Chinese)
[23］童峰, 陆佶人, 方世良. 水声目标识别中一种轴频提取方法[J]. 声学学报, 2004, 29(5):398-402.
TONG Feng, LU Ji-ren, FANG Shi-liang. A method to estimate the propeller shaft rate in underwater acoustic vessel classification[J]. Acta Acustica, 2004, 29(5):398-402.(in Chinese)
［24］卢言利，穆景阳. 基于遗传BP神经网络的隐身涂层老化性能评价模型［J］. 兵工学报，2015，36（8）：1580-1586.
LU Yan-li, MU Jing-yang. Evaluation model of aging properties of stealth coatings based on genetic BP neural network［J］. Acta Armamentarii, 2015,36(8): 1580-1586. (in Chinese)
[25］王波雷，马孝义，郝晶晶.基于L-M优化算法的喷头射程神经网络预测模型[J]. 农业机械学报, 2008,39(5) :35－40.
WANG Bo-lei, MA Xiao-yi, HAO Jing-jing. Prediction of sprinkler nozzle range based on L-M optimized algorithm[J].Transactions of the Chinese Society for Agricultural Machinery, 2008, 39(5): 35-40.(in Chinese)

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