CLIPC: Contrastive-Learning-Based Radar Signal Intrapulse Clustering

The radar signal intrapulse clustering (RSIPC) can help achieve unsupervised radar emitter identification, which is of great significance in the field of electronic warfare. In order to address the poor performance of traditional clustering methods in handling RSIPC tasks, we propose a contrastive learning-based RSIPC method called CLIPC. Since the single-domain information of radar signal intrapulses may result in the loss of important features, we integrate the multidomain information of radar signal intrapulses to obtain information fusion samples. By training a contrastive learning network on these information fusion samples, the network can extract deep features of radar signal intrapulses. Subsequently, we realize RSIPC using these deep features. To enhance the adaptability of the used contrastive learning network, we optimize the data augmentation methods in the network through experimental analysis. Additionally, we optimize the dimension of the deep features extracted by the network to reduce information redundancy and improve the efficiency of features clustering. Experimental results demonstrate that our improvements in contrastive learning network lead to better clustering performance and efficiency. We also investigate the clustering performance and reliability of the CLIPC under different signal-to-noise ratios (SNRs) through experiments. When the SNR is 0 dB, the proposed method has a clustering accuracy that is 0.1 higher than the contrastive learning method based on single-domain information, 0.3 higher than the traditional clustering method, and 0.2 higher than the clustering method based on autoencoder.