用几何参数生成高分辨雷达图像，提升海上目标识别稳定性
Conditional Generative Models for High-Resolution Range Profiles: Capturing Geometry-Driven Trends in a Large-Scale Maritime Dataset
- 以船体尺寸和视角为条件，生成高分辨率雷达剖面图
- 生成结果准确复现真实数据中的视线几何趋势
- 适合雷达目标识别、军事监测等场景研究者
高分辨率雷达剖面（HRRPs）可实现雷达目标自动识别的快速机上处理，但其对采集条件高度敏感，导致在不同作战场景下鲁棒性不足。条件化HRRP生成可缓解此问题，但现有研究受限于小规模、高度特定的数据集。本文基于大规模海事数据库，研究了海岸监视变化下的HRRP合成。分析表明，基本场景驱动因素为几何特性：船体尺寸与期望的观测角度。以这些变量为条件训练生成模型，结果显示合成的签名准确再现了真实数据中观察到的视线几何趋势。这些结果突显了采集几何在稳健HRRP生成中的核心作用。
原文摘要 · Abstract (English)
High-resolution range profiles (HRRPs) enable fast onboard processing for radar automatic target recognition, but their strong sensitivity to acquisition conditions limits robustness across operational scenarios. Conditional HRRP generation can mitigate this issue, yet prior studies are constrained by small, highly specific datasets. We study HRRP synthesis on a largescale maritime database representative of coastal surveillance variability. Our analysis indicates that the fundamental scenario drivers are geometric: ship dimensions and the desired aspect angle. Conditioning on these variables, we train generative models and show that the synthesized signatures reproduce the expected line-of-sight geometric trend observed in real data. These results highlight the central role of acquisition geometry for robust HRRP generation.
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