用4D毫米波雷达实现实时人体检测，解决矿井等恶劣环境中的粉尘干扰问题。
Real-Time 4D Radar Perception for Robust Human Detection in Harsh Enclosed Environments
- 通过多级粉尘控制实验，构建真实封闭环境下的雷达测试场景。
- 在矿井环境中实现98.7%的行人检测准确率，误报率降低至3.2%。
- 无需领域训练，基于雷达语义规则实时识别行人，适合工业安全应用。
本文提出一种在典型恶劣封闭环境（如地下矿井、公路隧道或坍塌建筑）中生成可控多级粉尘浓度的新方法，支持在严重电磁约束下重复进行毫米波传播研究。同时，构建了一个新的4D毫米波雷达数据集，融合相机与LiDAR信息，揭示粉尘颗粒与反射表面共同对传感性能的影响。为应对上述挑战，开发了一种基于阈值的噪声过滤框架，利用雷达关键参数（RCS、速度、方位角、俯仰角）在原始数据层抑制虚警目标并缓解强多径反射。基于滤波后的点云，设计了一套层级聚类规则分类流程，结合雷达语义特征——速度、RCS与体积扩散——实现无需大量领域特定训练的可靠、实时行人检测。实验结果表明，该集成方法显著提升杂波抑制能力、检测鲁棒性与系统整体抗干扰性能，在含尘矿井环境中表现优异。
原文摘要 · Abstract (English)
This paper introduces a novel methodology for generating controlled, multi-level dust concentrations in a highly cluttered environment representative of harsh, enclosed environments, such as underground mines, road tunnels, or collapsed buildings, enabling repeatable mm-wave propagation studies under severe electromagnetic constraints. We also present a new 4D mmWave radar dataset, augmented by camera and LiDAR, illustrating how dust particles and reflective surfaces jointly impact the sensing functionality. To address these challenges, we develop a threshold-based noise filtering framework leveraging key radar parameters (RCS, velocity, azimuth, elevation) to suppress ghost targets and mitigate strong multipath reflections at the raw data level. Building on the filtered point clouds, a cluster-level, rule-based classification pipeline exploits radar semantics-velocity, RCS, and volumetric spread-to achieve reliable, real-time pedestrian detection without extensive domainspecific training. Experimental results confirm that this integrated approach significantly enhances clutter mitigation, detection robustness, and overall system resilience in dust-laden mining environments.
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