Neuromorphic computing for radar and radio systems: a survey

Abstract Taking inspiration from the brain on how to create energy efficient and low latency neuromorphic systems has the potential to create new opportunities with AI across many domains. Firstly, it creates a possibility to mitigate problems with too large digital signal processing costs in various technologies. Secondly, it also enables the use of AI and machine learning algorithms where it is currently impossible due to energy constraints. Recently, neuromorphic technology has been introduced to radio communication and radar applications. In this work, we highlight advantages of applying energy efficient, low latency and often lightweight neuromorphic computing for radar and radio signal processing. We perform a comprehensive review of the main current works on neuromorphic technology for radar applications, focusing on frequency-modulated continuous-wave and synthetic aperture radar. Additionally, we cover radio frequency signal classification for both radar and radio signals. Our ambition is to facilitate research on neuromorphic computing for radar and radio systems, as well as help bringing researchers from these fields together.