Analyzing the Use of Fully Convolutional Neural Networks for Interference Mitigation in Automotive Radar

This article discusses the use of fully convolutional neural networks (CNNs) for interference mitigation in automotive radar. As the automotive industry continues to develop advanced driver assistance systems (ADAS) and autonomous vehicles, reliable and accurate radar sensing is crucial for ensuring the safety of these systems.

Frequency modulated continuous wave (FMCW) radar is a commonly used technology in automotive applications to determine the distance, velocity, and angle of objects around a vehicle. However, one challenge in using multiple radar sensors in close proximity is the potential for mutual interference, which can degrade the quality of predictions.

Previous work has focused on using neural networks (NNs) to mitigate interference by processing data from the entire receiver array in parallel. While effective, these architectures have limitations in generalizing well across different angles of arrival (AoAs) of interferences and objects.

In this paper, the authors propose a new architecture that combines fully convolutional neural networks with rank-three convolutions to transfer learned patterns between different AoAs. This architecture aims to achieve better performance, increased robustness, and a lower number of trainable parameters compared to previous approaches.

To evaluate the proposed network, the authors used a diverse dataset and demonstrated its angle equivariance, indicating that it can effectively handle interferences and objects at different angles of arrival. This is an important feature for automotive radar systems as they need to be able to accurately detect and track objects from various directions.

This research has significant implications for the development of interference mitigation techniques in automotive radar systems. By leveraging fully convolutional neural networks with rank-three convolutions, it is possible to improve the accuracy and reliability of radar sensing, ultimately enhancing overall safety in autonomous driving scenarios.

Looking ahead, further research could focus on optimizing the proposed architecture for real-time implementation, as well as exploring additional methods for interference mitigation in automotive radar. Additionally, the training and testing of the network on larger, more diverse datasets could provide further insights into its robustness and generalization capabilities.

Read the original article