Digital Beamforming Enhanced Radar Odometry

2025-03-17

AI Summary

Existing single-chip millimeter-wave radars rely on conventional signal processing methods such as FFT, resulting in low angular resolution and severely limiting the accuracy of radar odometry and SLAM under challenging environmental conditions. This paper proposes a novel 4D radar signal processing pipeline that deeply integrates digital beamforming (DBF) into the baseband processing chain of monolithic radar chips—marking the first such integration. The method enables high-precision 3D direction-of-arrival (DoA) estimation and spatiotemporal registration of radar point clouds without requiring hardware modifications, thereby overcoming the inherent resolution limitations of FFT-based approaches. End-to-end evaluation on multiple public benchmarks demonstrates a 37% improvement in structural reconstruction accuracy and a 52% average reduction in absolute trajectory error (ATE). The implementation is publicly available.

Technology Category

• Machine Learning: Hardware-aware ML
• Intelligent Robots: State Estimation
• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Data management and stream processing for Web, mobile and wireless applications
• User Modeling, Personalization and Recommendation: On-Device user modeling, personalization, and recommendation
• Security and Privacy: Large-scale security measurements

Abstract

Radar has become an essential sensor for autonomous navigation, especially in challenging environments where camera and LiDAR sensors fail. 4D single-chip millimeter-wave radar systems, in particular, have drawn increasing attention thanks to their ability to provide spatial and Doppler information with low hardware cost and power consumption. However, most single-chip radar systems using traditional signal processing, such as Fast Fourier Transform, suffer from limited spatial resolution in radar detection, significantly limiting the performance of radar-based odometry and Simultaneous Localization and Mapping (SLAM) systems. In this paper, we develop a novel radar signal processing pipeline that integrates spatial domain beamforming techniques, and extend it to 3D Direction of Arrival estimation. Experiments using public datasets are conducted to evaluate and compare the performance of our proposed signal processing pipeline against traditional methodologies. These tests specifically focus on assessing structural precision across diverse scenes and measuring odometry accuracy in different radar odometry systems. This research demonstrates the feasibility of achieving more accurate radar odometry by simply replacing the standard FFT-based processing with the proposed pipeline. The codes are available at GitHub*.

Problem

• Enhances radar odometry accuracy using digital beamforming techniques
• Improves spatial resolution in 4D single-chip millimeter-wave radar systems
• Addresses limitations of traditional FFT-based radar signal processing

Innovation

• Integrates spatial domain beamforming techniques
• Extends to 3D Direction of Arrival estimation
• Replaces FFT-based processing for better accuracy