Millimeter Wave Radar: From Synthetic Aperture to Probabilistic Mapping

2026-07-11

AI Summary

This work addresses the challenge of constructing accurate probabilistic occupancy maps in adverse environments such as smoke or dense fog, where conventional sensors often fail. While millimeter-wave radar offers robustness under such conditions, its signals are typically sparse and noisy, hindering high-fidelity mapping. To overcome this limitation, the paper presents the first end-to-end framework that integrates synthetic aperture radar (SAR) processing with probabilistic occupancy mapping. The authors systematically analyze the impact of antenna array configurations and key system parameters on mapping performance. Leveraging GPU acceleration for efficient computation, the proposed method is validated across multiple indoor scenarios, with map quality quantitatively assessed through path planning tasks. Additionally, the study introduces and publicly releases the first cascaded millimeter-wave radar dataset along with a complete open-source toolchain.

Abstract

Robust probabilistic mapping is essential for autonomous robotic systems operating in challenging environments. While traditional sensors fail in adverse conditions such as smoke and fog, millimeter wave (mmWave) radar sensors offer reliable sensing in such conditions. However, creating accurate probabilistic maps from radar data presents significant challenges due to the inherently sparse and noisy characteristics of radio wave measurements and signal processing steps. In an attempt to address these issues, we establish a complete pipeline from raw radar signals to probabilistic occupancy maps, incorporating Synthetic Aperture Radar processing followed by a probabilistic modeling step. We conduct extensive validation across indoor environments, comparing our approach against different signal processing and probabilistic modeling approaches. We also evaluate mapping quality through downstream path planning performance analysis. Furthermore, we investigate the impact of key parameters and antenna array configuration on mapping performance. The experimental results demonstrate both the effectiveness and limitations of SAR-based probabilistic mapping for real-world robotic deployment. To facilitate future research and broader adoption, we contribute an open-source cascaded mmWave radar dataset with an accompanying GPU-accelerated signal processing pipeline available at https://github.com/rpl-cmu/rpm.

Problem

• millimeter wave radar
• probabilistic mapping
• Synthetic Aperture Radar
• occupancy mapping
• autonomous robotics

Innovation

• millimeter wave radar
• synthetic aperture radar
• probabilistic mapping
• occupancy mapping
• GPU-accelerated pipeline