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

## TL;DR

This paper introduces a novel GAN-based method to enhance low-resolution radar data, effectively overcoming traditional design trade-offs in radar systems such as resolution and range limitations.

## Contribution

The work applies generative adversarial networks to radar super-resolution, a novel approach that improves data quality without increasing hardware complexity.

## Key findings

- Enhanced radar resolution using GANs
- Improved velocity and range-azimuth trade-offs
- Validated on micro-Doppler signatures and FMCW radars

## Abstract

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.