融合多域统计与深度特征，提升雷达小目标检测鲁棒性
Multi-Domain Features Guided Supervised Contrastive Learning for Radar Target Detection
- 用多域差异统计特征+监督对比学习深度特征联合建模
- 在真实海杂波数据上实现小目标检测率提升，跨环境性能更优
- 适合需要高鲁棒性的海上雷达目标检测场景
由于动态海洋环境，海杂波中检测小目标具有挑战性。现有方法或建模海杂波进行检测，或基于杂波-目标回波差异提取特征，包括统计特征和深度特征。后者虽在受控场景表现良好，但在多样化环境下检测鲁棒性和泛化能力不足，限制实际应用。本文提出一种多域特征引导的监督对比学习（MDFG_SCL）方法，将多域差异导出的统计特征与监督对比学习获得的深度特征融合，从而捕捉低层域特定变化和高层语义信息。该综合特征集成使模型能在复杂条件下有效区分小目标与海杂波。在真实世界数据集上的实验表明，所提出的浅层到深层检测器不仅实现了对小海面目标的有效识别，且在不同海况下保持优异检测性能，优于主流无监督对比学习和监督对比学习方法。
原文摘要 · Abstract (English)
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.
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