Complex Signal Denoising and Interference Mitigation for Automotive Radar Using Convolutional Neural Networks

TL;DR

This paper proposes using convolutional neural networks to improve automotive radar signal processing by effectively denoising and mitigating interference, outperforming traditional methods and enhancing sensor reliability in autonomous driving systems.

Contribution

The paper introduces a CNN-based approach for interference mitigation in automotive radar, trained on simulated data, demonstrating superior performance over existing techniques.

Findings

CNN-based method achieves better interference mitigation.

The approach outperforms traditional signal processing techniques.

Code and models are publicly available for further research.

Abstract

Driver assistance systems as well as autonomous cars have to rely on sensors to perceive their environment. A heterogeneous set of sensors is used to perform this task robustly. Among them, radar sensors are indispensable because of their range resolution and the possibility to directly measure velocity. Since more and more radar sensors are deployed on the streets, mutual interference must be dealt with. In the so far unregulated automotive radar frequency band, a sensor must be capable of detecting, or even mitigating the harmful effects of interference, which include a decreased detection sensitivity. In this paper, we address this issue with Convolutional Neural Networks (CNNs), which are state-of-the-art machine learning tools. We show that the ability of CNNs to find structured information in data while preserving local information enables superior denoising performance. To achieve this, CNN parameters are found using training with simulated data and integrated into the automotive radar signal processing chain. The presented method is compared with the state of the art, highlighting its promising performance. Hence, CNNs can be employed for interference mitigation as an alternative to conventional signal processing methods. Code and pre-trained models are available at https://github.com/johanna-rock/imRICnn.