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

arXiv: https://arxiv.org/abs/1906.10044 · HTML: https://mlsanity.com/paper/1906.10044/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 24 Jun 2019.

## Built on by (most cited first, 6 of 6)

- [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)
- [FMNet: Latent Feature-wise Mapping Network for Cleaning up Noisy Micro-Doppler Spectrogram](https://mlsanity.com/paper/2107.07312/) (2021)
- [GenRadar: Self-supervised Probabilistic Camera Synthesis based on Radar Frequencies](https://mlsanity.com/paper/2107.08948/) (2021)
- [Deep Learning for Joint Narrowband Interference Cancellation and Soft Demodulation in OFDM Systems](https://mlsanity.com/paper/2607.08717/) (2026)

## Abstract (arXiv)

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.