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.

翻译：工业和地下环境中的普适感知因空气中粉尘、烟雾、受限几何结构及金属构件而受到严重制约，这些因素会迅速劣化基于光学与激光雷达的感知性能。具备高程分辨能力的四维毫米波雷达对此类恶劣条件展现出强鲁棒性，然而如何对其稀疏且各向异性的点云进行处理，以在封闭且能见度退化的空间中实现可靠的人体检测，目前认知仍较为有限。本文提出一种完全模型驱动的四维雷达感知框架，专为在嵌入式边缘硬件上实时运行而设计。该系统以雷达作为唯一感知模态，集成了领域感知的多阈值滤波、自运动补偿的时间累积、结合多普勒感知优化的KD树欧几里得聚类，以及基于规则的三维分类器。该框架在充满粉尘的封闭拖车及真实地下采矿巷道中进行了评估，在测试场景中，当相机与激光雷达模态因严重能见度退化而失效时，基于雷达的检测器仍能保持稳定的行人识别能力。这些结果表明，所提出的模型驱动方法为恶劣工业及地下环境中的安全关键应用提供了鲁棒、可解释且计算高效的感知解决方案。