复杂雷达目标前向散射建模的内在散射体表示
Intrinsic Scatterer Representation for Forward Scattering Modeling of Complex Radar Targets
- Key Laboratory of Information Science of Electromagnetic Waves (Ministry of Education), Fudan University（复旦大学电磁波信息科学教育部重点实验室）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
本文提出一种基于内在散射体表示的前向散射建模框架，通过改进RANSAC等技术提取并优化散射体，可高效、可解释地生成复杂雷达目标的SAR图像，性能优于电磁仿真与实测数据对比结果。
AI中文摘要：
雷达目标散射中心的前向建模对合成孔径雷达（SAR）图像的高级信息检索至关重要。现有前向建模方法依赖于对目标进行网格化并通过射线追踪技术计算散射，这不仅会带来高计算成本，还会丢弃目标几何的语义信息，从而限制了SAR图像的可解释性。为解决这些问题，本文提出一种新颖的前向散射建模框架，该框架可直接从目标几何构建稳定、紧凑且具有物理意义的散射体。该公式将目标表示与特定观测配置解耦，从而实现跨不同观测角的内在散射体描述。具体而言，首先开发改进的随机抽样一致（RANSAC）方案，以从目标点云稳健提取平面、圆柱和球体，生成单次 bounce 散射体；随后通过分析基元间关系自动检测潜在的多次 bounce 散射体，并通过几何裁剪和参数对齐进一步优化，以生成与规范散射中心模型兼容的独特散射表示；最后，可基于构建的散射体在任意观测配置下生成雷达响应和SAR图像。所提方法通过大量实验验证，包括与电磁仿真和实测数据的对比，结果表明其能准确表征复杂目标的散射行为，为SAR图像建模与理解提供了高效、可解释且可靠的解决方案。
英文摘要：
Forward modeling of scattering centers of radar targets is critical for advanced information retrieval of Synthetic Aperture Radar (SAR) images. Existing forward modeling approaches rely on meshing the target and computing scattering via ray-tracing techniques, which not only incur high computational cost but also discard the semantic information of target geometry, thereby limiting the interpretability of SAR imagery. To address these issues, this paper proposes a novel forward scattering modeling framework that directly constructs stable, compact, and physically meaningful scatterers from target geometry. This formulation decouples target representation from specific observation configurations, enabling an intrinsic scatterer description across varying viewing angles. Specifically, an improved Random Sample Consensus (RANSAC) scheme is first developed to robustly extract planes, cylinders, and spheres from target point clouds, yielding single-bounce scatterers. Potential multiple-bounce scatterers are then automatically detected by analyzing inter-primitive relations, and further refined through geometric clipping and parameter alignment to produce unique scattering representations compatible with canonical scattering center models. Finally, radar responses and SAR images can be generated under arbitrary observation configurations based on the constructed scatterers. The proposed method is validated through extensive experiments, including comparisons with electromagnetic simulations and measured data. The results show that it can accurately characterize the scattering behaviors of complex targets, providing an efficient, interpretable, and reliable solution for SAR image modeling and understanding.
