Logo
ScienceStack
Table of Contents
Fetching ...
Sign In

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

Zhenan Liu, Yaodong Cui, Amir Khajepour, George Shaker

TL;DR

This work addresses robust human detection in dust-filled, enclosed environments where optical sensors fail. It proposes a real-time 4D mmWave radar system, a dust-generation dataset, and a multi-sensor setup augmented with a threshold-based noise filter and a KD-tree, rule-based clustering pipeline that operates without heavy training. Key contributions include the dust-generation methodology, the 4D radar dataset, the early-data filtering to suppress multipath ghosts, and a low-latency pedestrian classifier that relies on radar semantics rather than domain-specific training. Experimental results show improved clutter mitigation and detection robustness in dust-laden mining-like scenarios, highlighting practical potential for safety-critical industrial applications, and discuss limitations and future directions in temporal data and sensor fusion.

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.

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

TL;DR

This work addresses robust human detection in dust-filled, enclosed environments where optical sensors fail. It proposes a real-time 4D mmWave radar system, a dust-generation dataset, and a multi-sensor setup augmented with a threshold-based noise filter and a KD-tree, rule-based clustering pipeline that operates without heavy training. Key contributions include the dust-generation methodology, the 4D radar dataset, the early-data filtering to suppress multipath ghosts, and a low-latency pedestrian classifier that relies on radar semantics rather than domain-specific training. Experimental results show improved clutter mitigation and detection robustness in dust-laden mining-like scenarios, highlighting practical potential for safety-critical industrial applications, and discuss limitations and future directions in temporal data and sensor fusion.

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.
Paper Structure (6 sections, 2 equations, 4 figures)

This paper contains 6 sections, 2 equations, 4 figures.

Table of Contents

  1. Introduction
  2. Sensor Setup
  3. Data Preparation
  4. Radar Noise Filtering
  5. Rule-based Radar Object Classification
  6. Conclusion

Figures (4)

  • Figure 1: Framework of a real-time, standalone 4D mmWave radar perception system for human detection in harsh environment
  • Figure 2: The number of raw point cloud points registered from LiDAR and radar as dust levels rise and the number of pedestrians increases.
  • Figure 3: Interference of dust in close-spaced environment to (a) camera, (b) LiDAR and (c) 4D radar. Compared to the circled position of individuals at (b) and (c), while LiDAR lost its detection ability, 4D radar remained consistent.
  • Figure 4: Number of detected pedestrians as dust level increases from (a) YOLOv8 object detection model, (b) standalone radar perception