Fingerprint Feature Recognition of Power Amplifier Based on One-dimensional Convolutional Neural Network

Abstract

Traditional communication station recognition usually needs to manually extract features and then use pattern recognition for classification. There are problems of complex preprocessing and difficult feature extraction. Power amplifiers are the core components of shortwave radio stations, and their fingerprint features can be used as radio stations. For the characteristics of individual recognition, a method of power amplifier fingerprint recognition based on one-dimensional convolutional neural network (1D-CNN) is proposed. The characteristic is that it can directly learn features from the original signal and finally complete the classification and recognition. By establishing a power amplifier model, the shortwave signal can obtain fingerprint characteristics. A four-layer 1D-CNN network is used to extract signal features, the network parameters are optimized through experiments, and finally the Softmax classifier is used for classification and recognition. A 98% recognition rate was obtained through experiments, which verified the effectiveness of the method.