Interference-Aware Cognitive Radar: A Remedy to the Automotive Interference Problem

Abstract

With the advancement of autonomous driving, the number of automotive radar sensors grows dramatically. As a result, interference becomes a major issue that has a vital importance for the future of automotive radar. Parallel to this development, radar concepts based on the digital signal generation and processing such as orthogonal frequency-division multiplexing (OFDM) have been studied over the past few years. The realization of the radar functionality based on digital waveforms provides a large degree of flexibility as well as adaptability. Among others, this can be used for increasing the interference robustness of digital radar systems compared to the traditional automotive radar. In this article, we propose a novel approach to mitigating automotive radar interference based on cognitive interference avoidance, which we demonstrate for the digital OFDM radar. We introduce the main building blocks of such a cognitive radar and focus specifically on waveform adaptation. The proposed waveform adaptation methods enable dynamical adjustment to interference, including adaptation during a single measurement cycle. The feasibility and performance of the proposed concepts are studied in simulations and validated using measurements with an OFDM radar prototype.