应用深度神经网络和集成学习的电台个体识别

提出了一种基于深度神经网络的个体智能识别方法,可用于电台个体分类识别.该方法构建集成多子网络的一维深度卷积模型,以电台时序信号作为模型输入,进行电台个体分类.利用深度神经网络自动特征化的能力,该方法从时序信号中自动获取个体特征,从而以端到端的形式实现从电台信号识别电台个体.该方法能够免去基于专家知识的特征提取工作,自动...

Radio individual recognition based on deep neural network and ensemble learning

A radio individual recognition method based on deep neural network model is proposed.This one-dimensional deep neural network model consists of multiple sub-networks.Radio signal time series are input to this model,and the radio corresponding to each input signal is thereafter recognized.This method utilizes the capability of automatically extracting data features by deep neural networks,to extract fingerprint features from radio signal time series,thus recognizing radio individuals from signal series in an end-to-end manner.Consequently,this method can save feature extraction labor which relies on expert knowledge,and can automatically extract deep features to help recognize very similar radio individuals,which can hardly be distinguished through very similar conventional features.Experiments suggest this method is effective in simplifying parameter adjustment in model optimizing,alleviating over-fitting by single network,and improving generalization and robustness of the radio recognition algorithm.Under the circumstance of 12 dB signal-to-noise ratio(SNR),8PSK modulated signals from 10 radio individuals are classified,where overall accuracy is 91.83% and mean accuracy is 89.12%.Under the same SNR,MSK modulated signals are classified with 89.1% overall accuracy.