基于稀疏自动编码网络的水声通信信号调制识别

研究了基于稀疏自动编码网络的水声通信信号识别方法。首先利用稀疏自动编码网络对接收信号的功率谱识别分类，得到除PSK外信号的调制类型，然后对识别结果为PSK的信号做四次方谱，最后利用稀疏自动编码网络完成对QPSK和8PSK的识别分类。仿真实验表明，稀疏自动编码网络能从接收信号的谱信息中自动提取有效谱特征。与传统基于功率谱特征提取的识别方法相比，本文算法减少了依赖领域知识的特征提取环节，识别性能优于传统算法。 

Underwater Communication Signals’ Modulation Recognition Based on Sparse Autoencoding Network

This paper studies the method of underwater communication signals’ recognition based on sparse autoencoding network. Firstly, the power spectrums of the received signals are classified using a sparse autoencoding network, getting modulation types of signals except of PSK. Then make the quartic spectrums of the signals whose recognition results are PSK in the first step. Finally, the classification of QPSK and 8PSK’s quartic spectrums is completed by using another sparse autoencoding network. Simulation experiments show that the sparse autoencoding network can automatically extract effective spectrum features from the received signals’ spectrum information. Compared with the traditional recognition method based on power spectrum feature extraction, the proposed algorithm reduces the feature extraction process based on domain knowledge, and the recognition performance is better than the traditional algorithm.