Year: 2025
Type: article
Abstract: Deep learning for fine-grained radar mode recognition faces a major bottleneck-its heavy reliance on expensively labeled data. To address this, we propose a novel semi-supervised framework that effectively leverages unlabeled data. Through an end-to-end, triple-branch framework that integrates a dual contrastive learning mechanism with tailored strategies for pulse distortions, our model achieves high accuracy with minimal labels. Experimental results on two challenging datasets demonstrate that the proposed framework boosts accuracy by 17% to 34% using only 10% of the labeled data, establishing a new state-of-the-art performance.
Source: Sensors
DOI: 10.3390/s25247440
Authors: Peishan Sun, Mingyang Du, Zhihui Li, Xuan Chen, Junpeng Shi
Institution: National University of Defense Technology
Language: English
Subfield: Aerospace Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $3,137
Award: G6598289231
OpenAlex ID: W4417120339