Advancing High-Resolution and Efficient Automotive Radar Imaging through Domain-Informed 1D Deep Learning
Ruxin Zheng, Shunqiao Sun, Hongshan Liu, Holger Caesar, Honglei Chen, Jian Li
Abstract
Millimeter-wave (mmWave) radars are critical for autonomous vehicles’ perception tasks, offering reliable performance in adverse weather conditions. However, their application is often hindered by insufficient spatial resolution for detailed semantic scene interpretation. Traditional super-resolution methods derived from optical imaging fail to accommodate the unique properties of radar signals. Addressing this, our study redefines radar imaging super-resolution as a one-dimensional (1D) signal super-resolution spectra estimation problem, leveraging domain-specific insights to innovate data normalization and introduce a domain-informed signal-to-noise ratio (SNR)-guided loss function. Our custom deep learning network, tailored for automotive radar imaging, achieves substantial improvements in parameter efficiency, and inference speed while enhancing image quality and resolution. Comprehensive tests demonstrate that our SR-SPECNet establishes a new standard for high-resolution radar range-azimuth imaging, surpassing previous methods. Source code and new radar dataset will be made publicly available at https://github.com/ruxinzh/SR_DOA.
BibTeX
@inproceedings{icassp2025_advancinghighres,
title = {Advancing High-Resolution and Efficient Automotive Radar Imaging through Domain-Informed 1D Deep Learning},
author = {Ruxin Zheng and Shunqiao Sun and Hongshan Liu and Holger Caesar and Honglei Chen and Jian Li},
booktitle = {ICASSP 2025},
year = {2025}
}