Contrastive Semi-Supervised Learning With Pseudo-Label for Radar Signal Automatic Modulation Recognition

Abstract

With the implementation of deep learning (DL) in signal recognition, the processing efficiency and accuracy of radar automatic modulation recognition (AMR) have been effectively improved. The success of DL usually depends on plentiful labeled training data, but, in actual electromagnetic environments, due to expensive annotation cost and noncooperative characteristics, there is often a scarcity of labeled samples and an abundance of unlabeled samples. Therefore, this article designs a contrastive semi-supervised learning (SSL) method that employs unlabeled samples with predicted pseudo-label to assist model training. First, the convolutional attention network is pretrained utilizing the contrastive learning method, and positive sample pairs are constructed by adding noise and rotation. Then, the pretrained backbone network and stochastically initialized classifier are fine-tuned with labeled samples. In order to avoid the influence of false negative sample pairs, the fine-tuned network is employed to predict unlabeled samples. The filtered samples with pseudo-label and real label samples constitute a new dataset through stratified sampling to balance the samples. Finally, the network is fine-tuned again using the mixed dataset. By comparing the proposed algorithm with existing supervised and semi-supervised methods on a simulation dataset, the algorithm can significantly improve the recognition performance under conditions of small sample size and low signal-to-noise ratio (SNR), verifying its superiority and robustness.