Multi-Domain Features Guided Supervised Contrastive Learning for Radar Target Detection
This work addresses robust detection and generalization for small maritime targets in diverse environments, which is an incremental improvement over existing methods.
The paper tackles the problem of detecting small targets in sea clutter under dynamic maritime conditions by proposing a multi-domain features guided supervised contrastive learning method, which integrates statistical and deep features to improve detection. Experiments on real-world datasets show it outperforms mainstream unsupervised and supervised contrastive learning methods in maintaining superior detection performance across varying sea conditions.
Detecting small targets in sea clutter is challenging due to dynamic maritime conditions. Existing solutions either model sea clutter for detection or extract target features based on clutter-target echo differences, including statistical and deep features. While more common, the latter often excels in controlled scenarios but struggles with robust detection and generalization in diverse environments, limiting practical use. In this letter, we propose a multi-domain features guided supervised contrastive learning (MDFG_SCL) method, which integrates statistical features derived from multi-domain differences with deep features obtained through supervised contrastive learning, thereby capturing both low-level domain-specific variations and high-level semantic information. This comprehensive feature integration enables the model to effectively distinguish between small targets and sea clutter, even under challenging conditions. Experiments conducted on real-world datasets demonstrate that the proposed shallow-to-deep detector not only achieves effective identification of small maritime targets but also maintains superior detection performance across varying sea conditions, outperforming the mainstream unsupervised contrastive learning and supervised contrastive learning methods.