Abstract
In automotive applications, frequency modulated continuous wave (FMCW) radar is an established technology to determine the distance, velocity and angle of objects in the vicinity of the vehicle. The quality of predictions might be seriously impaired if mutual interference between radar sensors occurs. Previous work processes data from the entire receiver array in parallel to increase interference mitigation quality using neural networks (NNs). However, these architectures do not generalize well across different angles of arrival (AoAs) of interferences and objects. In this paper we introduce fully convolutional neural network (CNN) with rank-three convolutions which is able to transfer learned patterns between different AoAs. Our proposed architecture outperforms previous work while having higher robustness and a lower number of trainable parameters. We evaluate our network on a diverse data set and demonstrate its angle equivariance.
Original language | English |
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Title of host publication | 2023 20th European Radar Conference (EuRAD) |
Pages | 135-138 |
Number of pages | 4 |
ISBN (Electronic) | 9782874870743 |
DOIs | |
Publication status | Published - 2023 |
Event | 20th European Radar Conference: EuRAD 2023 - Berlin, Germany Duration: 20 Sept 2023 → 22 Sept 2023 |
Conference
Conference | 20th European Radar Conference |
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Abbreviated title | EuRAD 2023 |
Country/Territory | Germany |
City | Berlin |
Period | 20/09/23 → 22/09/23 |
Keywords
- angle-equivariance
- complex-valued processing
- convolutional neural networks
- deep learning
- FMCW radar
- interference mitigation
ASJC Scopus subject areas
- Instrumentation
- Computer Networks and Communications