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

arXiv: https://arxiv.org/abs/2406.07399 · HTML: https://mlsanity.com/paper/2406.07399/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 38 references that are in the map.
- First posted on arXiv 11 Jun 2024.

## Builds on (most cited first, 10 of 10)

- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [nuScenes: A multimodal dataset for autonomous driving](https://mlsanity.com/paper/1903.11027/) (2019)
- [The Oxford Radar RobotCar Dataset: A Radar Extension to the Oxford RobotCar Dataset](https://mlsanity.com/paper/1909.01300/) (2019)
- [K-Radar: 4D Radar Object Detection for Autonomous Driving in Various Weather Conditions](https://mlsanity.com/paper/2206.08171/) (2022)
- [RADIATE: A Radar Dataset for Automotive Perception in Bad Weather](https://mlsanity.com/paper/2010.09076/) (2020)
- [Raw High-Definition Radar for Multi-Task Learning](https://mlsanity.com/paper/2112.10646/) (2021)
- [RadarScenes: A Real-World Radar Point Cloud Data Set for Automotive Applications](https://mlsanity.com/paper/2104.02493/) (2021)
- [CARRADA Dataset: Camera and Automotive Radar with Range-Angle-Doppler Annotations](https://mlsanity.com/paper/2005.01456/) (2020)
- [An Adversarial Super-Resolution Remedy for Radar Design Trade-offs](https://mlsanity.com/paper/1903.01392/) (2019)
- [Doppler-Resilient 802.11ad-Based Ultra-Short Range Automotive Joint Radar-Communications System](https://mlsanity.com/paper/1902.01306/) (2019)

## Built on by (most cited first, 1 of 1)

- [Predictive Modeling of Maritime Radar Data Using Transformer Architecture](https://mlsanity.com/paper/2512.17098/) (2025)

## Abstract (arXiv)

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.