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。
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.
作者
江伟华
童峰
王彬
刘世刚
JIANG Wei-hua TONG Feng WANG Bin LIU Shi-gang(Key Laboratory of Underwater Acoustic Communication and Marine Information Technology of Ministry of Education Xiamen University, Xiamen 361005, Fujian, China The PLA Information Engineering University, Zhengzhou 450002, Henan,China)
出处
《兵工学报》
EI
CAS
CSCD
北大核心
2016年第9期1670-1676,共7页
Acta Armamentarii
基金
国家自然科学基金项目(11274259
11574258)
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
acoustics
underwater acoustic digital modulated signal
spectrum feature
modulation rec- ognition
principal component analysis
ANN classifier