# An Adversarial Super-Resolution Remedy for Radar Design Trade-offs

arXiv: https://arxiv.org/abs/1903.01392 · HTML: https://mlsanity.com/paper/1903.01392/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 4 Mar 2019.

## Built on by (most cited first, 6 of 6)

- [Coherent, super resolved radar beamforming using self-supervised learning](https://mlsanity.com/paper/2106.13085/) (2021)
- [SLPC: a VRNN-based approach for stochastic lidar prediction and completion in autonomous driving](https://mlsanity.com/paper/2102.09883/) (2021)
- [Efficient CNN-based Super Resolution Algorithms for mmWave Mobile Radar Imaging](https://mlsanity.com/paper/2305.02092/) (2023)
- [Uncertainty-Based Biological Age Estimation of Brain MRI Scans](https://mlsanity.com/paper/2103.08491/) (2021)
- [Redefining Automotive Radar Imaging: A Domain-Informed 1D Deep Learning Approach for High-Resolution and Efficient Performance](https://mlsanity.com/paper/2406.07399/) (2024)
- [GenRadar: Self-supervised Probabilistic Camera Synthesis based on Radar Frequencies](https://mlsanity.com/paper/2107.08948/) (2021)

## Abstract (arXiv)

Radar is of vital importance in many fields, such as autonomous driving, safety and surveillance applications. However, it suffers from stringent constraints on its design parametrization leading to multiple trade-offs. For example, the bandwidth in FMCW radars is inversely proportional with both the maximum unambiguous range and range resolution. In this work, we introduce a new method for circumventing radar design trade-offs. We propose the use of recent advances in computer vision, more specifically generative adversarial networks (GANs), to enhance low-resolution radar acquisitions into higher resolution counterparts while maintaining the advantages of the low-resolution parametrization. The capability of the proposed method was evaluated on the velocity resolution and range-azimuth trade-offs in micro-Doppler signatures and FMCW uniform linear array (ULA) radars, respectively.