# A Deep Learning Approach for Automotive Radar Interference Mitigation

arXiv: https://arxiv.org/abs/1903.06380 · HTML: https://mlsanity.com/paper/1903.06380/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 15 Mar 2019.

## Built on by (most cited first, 5 of 5)

- [Interference Suppression Using Deep Learning: Current Approaches and Open Challenges](https://mlsanity.com/paper/2112.08988/) (2021)
- [Complex-valued Convolutional Neural Networks for Enhanced Radar Signal Denoising and Interference Mitigation](https://mlsanity.com/paper/2105.00929/) (2021)
- [Resource-efficient Deep Neural Networks for Automotive Radar Interference Mitigation](https://mlsanity.com/paper/2201.10360/) (2022)
- [End-to-End Training of Neural Networks for Automotive Radar Interference Mitigation](https://mlsanity.com/paper/2312.09790/) (2023)
- [Angle-Equivariant Convolutional Neural Networks for Interference Mitigation in Automotive Radar](https://mlsanity.com/paper/2401.05385/) (2023)

## Abstract (arXiv)

In automotive systems, a radar is a key component of autonomous driving. Using transmit and reflected radar signal by a target, we can capture the target range and velocity. However, when interference signals exist, noise floor increases and it severely affects the detectability of target objects. For these reasons, previous studies have been proposed to cancel interference or reconstruct original signals. However, the conventional signal processing methods for canceling the interference or reconstructing the transmit signals are difficult tasks, and also have many restrictions. In this work, we propose a novel approach to mitigate interference using deep learning. The proposed method provides high performance in various interference conditions and has low processing time. Moreover, we show that our proposed method achieves better performance compared to existing signal processing methods.