Few-label Radar Work Mode Recognition based on multi-task contrastive learning

To address the challenge of recognizing Active Electronically Scanned Arrays (AESA) radar work modes under few labeled data, this paper proposes a semi-supervised algorithm based on multi-task contrastive learning. The method employs a dual-stage collaborative optimization framework combining self-supervised pre-training and supervised fine-tuning to effectively leverage latent feature information from unlabeled data. In the self-supervised pre-training stage, a dual-path data augmentation strategy is designed to generate mode-consistent pulse sequence samples, where an expander and a base encoder work synergistically to capture both temporal dependencies and cross-pulse deep correlation features. Additionally, three self-supervised tasks—contextual contrastive learning, pulse sequence reconstruction, and augmentation feature prediction—are introduced to optimize feature space distribution, disentangle noise interference, and enforce consistency constraints across augmented samples, respectively. During the supervised fine-tuning stage, a classifier integrates self-supervised representations with few labeled data to enhance recognition performance. Comparative experiments demonstrate that the proposed algorithm achieves significantly higher recognition accuracy on the AESA radar work mode dataset compared to other mainstream semi-supervised methods under few labeled conditions. Ablation studies validate the strengthening effect of the multi-task collaborative mechanism on model performance. Additionally, data augmentation comparison experiments identify the optimal augmentation strategy for this algorithm.