用多维微动特征区分真船与角反射器诱饵，提升雷达反干扰能力。
Corner Reflector Array Jamming Discrimination Using Multi-Dimensional Micro-Motion Features with Frequency Agile Radar
- 结合手工特征与轻量CNN深度特征，融合多维微动信息
- 在仿真中性能超越现有方法，识别准确率显著提升
- 适合雷达对抗、海上目标识别等场景应用
本文提出一种基于频率敏捷雷达的稳健判别方法，用于区分真实舰船目标与角反射器阵列诱饵。核心思路是利用能区分刚性舰船与非刚性诱饵的多维微动特征。从距离-速度图中提取两个新设计的手工特征：均值加权残差（MWR）和互补对比因子（CCF），并与轻量级CNN学习的深度特征融合。最终通过XGBoost分类器做出判断。大量仿真实验表明，该混合特征集持续优于现有先进方法，验证了所提方案的优越性。
原文摘要 · Abstract (English)
This paper introduces a robust discrimination method for distinguishing real ship targets from corner-reflector-array jamming with frequency-agile radar. The key idea is to exploit the multidimensional micro-motion signatures that separate rigid ships from non-rigid decoys. From Range-Velocity maps we derive two new hand-crafted descriptors-mean weighted residual (MWR) and complementary contrast factor (CCF) and fuse them with deep features learned by a lightweight CNN. An XGBoost classifier then gives the final decision. Extensive simulations show that the hybrid feature set consistently outperforms state-of-the-art alternatives, confirming the superiority of the proposed approach.
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