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.

翻译：本文提出一种创新方法，可在高度杂波环境中生成受控的多层级粉尘浓度，该环境模拟了地下矿井、公路隧道或坍塌建筑等恶劣封闭场景，从而能够在严苛电磁约束条件下开展可重复的毫米波传播研究。我们同时发布了一个新的4D毫米波雷达数据集，该数据集通过相机与激光雷达进行增强，揭示了粉尘颗粒与反射表面如何共同影响感知功能。为应对这些挑战，我们开发了一种基于阈值的噪声过滤框架，该框架利用关键雷达参数（雷达截面积、速度、方位角、俯仰角）在原始数据层面抑制虚警目标并减轻强多径反射效应。基于过滤后的点云数据，我们构建了聚类层级的规则分类流程，通过挖掘雷达语义特征——速度、雷达截面积及体积分布——实现了无需大量领域特定训练的实时可靠行人检测。实验结果证实，这种集成方法在粉尘弥漫的采矿环境中显著提升了杂波抑制能力、检测鲁棒性及系统整体抗干扰性能。