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

2025-07-13

2025 IEEE International Symposium on Antennas and Propagation and North American Radio Science Meeting (AP-S/CNC-USNC-URSI)

AI Summary

This work addresses the challenge of reliable human detection in enclosed, dust-intensive environments with severe multipath interference—such as mines and tunnels—where conventional sensors often fail. The authors present the first reproducible experimental platform for electromagnetic wave propagation under high-dust conditions and propose an unsupervised, real-time detection framework based on raw 4D millimeter-wave radar data. By integrating a threshold-based filtering mechanism that leverages radar cross-section (RCS), velocity, azimuth, and elevation, along with cluster-level rule-based classification and advanced multipath/ghost suppression techniques, the system achieves robust human perception without requiring extensive domain-specific training data. Furthermore, fusion with LiDAR and camera inputs significantly enhances clutter rejection and detection reliability, enabling low-latency, high-accuracy human detection in real-world mine scenarios.

Technology Category

• Intelligent Robots: Multimodal Perception & Sensor Fusion
• Humans and AI: Planning and Decision Support for Human-Machine Teams
• Machine Learning: Hardware-aware ML

Application Category

• Economics, Online Markets and Human Computation: Trust and reliance of crowd workers and data experts on GenAI
• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Web Mining and Content Analysis: Web data integration and cleaning

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.

Problem

• 4D radar
• human detection
• harsh enclosed environments
• dust interference
• multipath reflections

Innovation

• 4D mmWave radar
• dust-laden environment
• noise filtering
• multipath mitigation
• real-time pedestrian detection