水声通信信号识别具有重要的现实意义。传统的识别器是每种调制模式分别设计的检测器,因而运算量随调制模式数量的增加而增加。随着通信技术手段日益更新,调制模式不断翻新,传统识别器已经不能满足快速检测和识别信号的要求,设计统一的特征提取方法以减少检测器的数量非常迫切。从侦收信号的时频分布中提取特征向量,利用人工神经网络对特征向量进行分类,基于此提出了一种新的识别方法。对本文提出的识别器,在使用前增加新的调制模式的样本并重新训练神经网络,使用过程中能实现更多调制模式的识别而不增加运算量。对特征向量的提取方法进行了详细描述,并通过计算机仿真实验,得出了低信噪比时的正确识别概率。

The fast detection of communication signals and the exact identification of their modulation types are of importance in practice. Traditional designs use detectors for each modulation type separately thus the computation time would increase as the number of modulation types increases. It is necessary to work out a standard feature vector extraction method to reduce the number of detectors. A novel identification scheme is proposed, with feature vectors being extracted from the time-frequency distribution and identified by an artificial neural network. By adding signal samples of new modulation types and by retraining the neural network, this identification scheme can recognize more modulation types without increase of computation burden. The detail of this feature vector extraction approach is described, the probability of the correct identification of the communication signals in low signal-to-noise conditions is obtained through computer simulations.