An Adversarial Super-Resolution Remedy for Radar Design Trade-offs
This work addresses radar performance limitations for applications like autonomous driving and surveillance, offering a method to bypass design constraints, though it is incremental as it applies existing GAN techniques to radar data.
The paper tackles radar design trade-offs, such as bandwidth limitations affecting range and resolution, by using generative adversarial networks (GANs) to enhance low-resolution radar data into higher resolution, achieving improvements in velocity resolution and range-azimuth trade-offs for micro-Doppler signatures and FMCW ULA radars.
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.