Redefining Automotive Radar Imaging: A Domain-Informed 1D Deep Learning Approach for High-Resolution and Efficient Performance

TL;DR

This paper introduces a domain-informed 1D deep learning approach for automotive radar super-resolution, significantly improving image quality and resolution while maintaining efficiency, scalability, and fast inference.

Contribution

It redefines radar super-resolution as a 1D spectra estimation problem using domain knowledge, novel normalization, and a SNR-guided loss function, setting a new benchmark.

Findings

Outperforms existing methods in radar image resolution and quality

Achieves high scalability and fast inference

Sets a new benchmark across various configurations

Abstract

Millimeter-wave (mmWave) radars are indispensable for perception tasks of autonomous vehicles, thanks to their resilience in challenging weather conditions. Yet, their deployment is often limited by insufficient spatial resolution for precise semantic scene interpretation. Classical super-resolution techniques adapted from optical imaging inadequately address the distinct characteristics of radar signal data. In response, our study redefines radar imaging super-resolution as a one-dimensional (1D) signal super-resolution spectra estimation problem by harnessing the radar signal processing domain knowledge, introducing innovative data normalization and a domain-informed signal-to-noise ratio (SNR)-guided loss function. Our tailored deep learning network for automotive radar imaging exhibits remarkable scalability, parameter efficiency and fast inference speed, alongside enhanced performance in terms of radar imaging quality and resolution. Extensive testing confirms that our SR-SPECNet sets a new benchmark in producing high-resolution radar range-azimuth images, outperforming existing methods across varied antenna configurations and dataset sizes. Source code and new radar dataset will be made publicly available online.