Abstract:

Radar modulation recognition technology plays a large part in electronic reconnaissance,electronic support and other traditional areas.Existing radar modulation signal recognition algorithms are usually implemented using intra-pulse feature extraction or deep learning techniques.Both methods have disadvantages.Extracting intra-pulse features requires complex prior knowledge.Although deep learning technology does not require complex prior knowledge,it is data-driven,which needs massive data to support its training,and it is very difficult to obtain radar signal data.It is also difficult to build a complex and huge data set to represent the model.Therefore,the need for a small-sample recognition method for deep learning technology becomes urgent.In this paper,we propose an algorithm for radar modulation signal recognition based on Enhanced Deep Convolution Generative Adversarial Networks (SDCGAN) and the Convolutional Neural Network (CNN) to achieve data enhancement.Under the condition of small samples,it is still possible to achieve high-precision identification of a variety of radar modulated signals.This paper verifies the superiority of the SDCGAN-CNN algorithm over other algorithms and the effectiveness of signal recognition under small sample conditions through comparative experiments.Under the condition of a relatively high signal-to-noise ratio,the recognition accuracy rate of other generation adversarial network and convolutional neural network methods is improved by 4%,and the recognition accuracy rate of convolutional neural network methods is increased by 10%.

Key words: signal recognition, generative adversarial network, small sample, convolutional neural network, data enhancement