Real-Time 4D Radar Perception for Robust Human Detection in Harsh Enclosed Environments

TL;DR

This paper presents a real-time 4D radar perception system that robustly detects humans in harsh, dust-filled environments by combining novel dust generation, a new radar dataset, noise filtering, and rule-based classification.

Contribution

It introduces a controlled dust environment, a new multi-modal radar dataset, and a noise filtering plus rule-based classification framework for robust human detection in challenging conditions.

Findings

Enhanced clutter mitigation and detection robustness in dust environments

Effective real-time pedestrian detection without extensive training

Significant improvement over existing methods in harsh environments

Abstract

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.