Adaptive Clustering With Similarity Learning for Enhanced Multi-Scenario Radar Signal Processing

In the complex and dynamic electromagnetic environment of modern electronic reconnaissance systems, highdensity and highly intertleaving signals cause challenges to radar signal sorting (RSS). This paper introduces a novel RSS method based on similarity learning and pseudo-labeling to address the pulse deinterleaving problem in complex scenarios. The method is divided into two stages: first, unsupervised clustering is used to generate pseudo-labels necessary for calculating the pulse repetition interval (PRI); then, a two-branch network based on similarity learning uses the PRI and other signal features to determine whether two clusters correspond to the same radar. By combining pseudo-labeling with similarity learning, the method improves the performance in high-density and highly interleaving environments, provides better robustness and calculation time, and thus effectively meets the requirements of current electronic reconnaissance tasks