DeepAI AI Chat
Log In Sign Up

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

by   Sherif Abdulatif, et al.
University of Stuttgart

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.


page 2

page 3

page 4

page 5


Mind the Gap: Trade-Offs between Distributed Ledger Technology Characteristics

While design decisions determine the quality and viability of applicatio...

Practical Trade-Offs for the Prefix-Sum Problem

Given an integer array A, the prefix-sum problem is to answer sum(i) que...

Sub-Nyquist Radar Systems: Temporal, Spectral and Spatial Compression

Conventional radar transmits electromagnetic waves towards the targets o...

Near out-of-distribution detection for low-resolution radar micro-Doppler signatures

Near out-of-distribution detection (OOD) aims at discriminating semantic...

Towards Adversarial Denoising of Radar Micro-Doppler Signatures

Generative Adversarial Networks (GANs) are considered the state-of-the-a...