基于4D雷达的轻量级框架，可在恶劣环境中稳定检测人体。
A Lightweight Model-Driven 4D Radar Framework for Pervasive Human Detection in Harsh Conditions
- 纯雷达感知，结合多阈值滤波与运动补偿积累
- 在粉尘遮蔽和地下矿道中保持行人识别稳定性
- 适合工业与地下场景的安全监测应用
工业与地下环境中普遍存在粉尘、烟雾、狭小空间和金属结构，严重限制了光学与激光雷达的感知能力。高分辨4D毫米波雷达具备强抗干扰能力，但其稀疏且各向异性的点云数据处理仍缺乏深入理解。本文提出一种完全模型驱动的4D雷达感知框架，专为嵌入式边缘设备实时运行设计。系统仅依赖雷达输入，融合领域感知的多阈值滤波、自车运动补偿的时间累积、基于KD树的欧氏聚类与多普勒感知优化，以及规则驱动的3D分类器。在充满粉尘的封闭拖车及真实地下矿道中测试，雷达检测器在摄像头与激光雷达失效时仍能稳定识别行人。结果表明，该模型驱动方法在恶劣工业与地下环境中具备鲁棒性、可解释性与计算高效性，适用于安全关键任务。
原文摘要 · Abstract (English)
Pervasive sensing in industrial and underground environments is severely constrained by airborne dust, smoke, confined geometry, and metallic structures, which rapidly degrade optical and LiDAR based perception. Elevation resolved 4D mmWave radar offers strong resilience to such conditions, yet there remains a limited understanding of how to process its sparse and anisotropic point clouds for reliable human detection in enclosed, visibility degraded spaces. This paper presents a fully model-driven 4D radar perception framework designed for real-time execution on embedded edge hardware. The system uses radar as its sole perception modality and integrates domain aware multi threshold filtering, ego motion compensated temporal accumulation, KD tree Euclidean clustering with Doppler aware refinement, and a rule based 3D classifier. The framework is evaluated in a dust filled enclosed trailer and in real underground mining tunnels, and in the tested scenarios the radar based detector maintains stable pedestrian identification as camera and LiDAR modalities fail under severe visibility degradation. These results suggest that the proposed model-driven approach provides robust, interpretable, and computationally efficient perception for safety-critical applications in harsh industrial and subterranean environments.
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