Signal Processing Challenges in Automotive Radar

TL;DR

This paper reviews key signal processing challenges in automotive radar, highlighting areas like modulation, interference, resolution, and deep learning, serving as a comprehensive overview for researchers and practitioners.

Contribution

It provides a broad overview of current research challenges and opportunities in automotive radar signal processing, including recent advances and future directions.

Findings

Discussion of modulation scheme improvements

Analysis of interference avoidance techniques

Potential of deep learning in radar signal processing

Abstract

As automotive radars continue to proliferate, there is a continuous need for improved performance and several critical problems that need to be solved. All of this is driving research across industry and academia. This paper is an overview of research areas that are centered around signal processing. We discuss opportunities in the area of modulation schemes, interference avoidance, spatial resolution enhancement and application of deep learning. A rich list of references is provided. This paper should serve as a useful starting point for signal processing practitioners looking to work in the area of automotive radars.