# Complex-valued Convolutional Neural Networks for Enhanced Radar Signal Denoising and Interference Mitigation

arXiv: https://arxiv.org/abs/2105.00929 · HTML: https://mlsanity.com/paper/2105.00929/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 26 references that are in the map.
- First posted on arXiv 29 Apr 2021.

## Builds on (most cited first, 9 of 9)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift](https://mlsanity.com/paper/1502.03167/) (2015)
- [Deep Complex Networks](https://mlsanity.com/paper/1705.09792/) (2017)
- [Enhanced Radar Imaging Using a Complex-valued Convolutional Neural Network](https://mlsanity.com/paper/1712.10096/) (2017)
- [Complex spectrogram enhancement by convolutional neural network with multi-metrics learning](https://mlsanity.com/paper/1704.08504/) (2017)
- [Fully Convolutional Neural Networks for Automotive Radar Interference Mitigation](https://mlsanity.com/paper/2007.11102/) (2020)
- [Complex Signal Denoising and Interference Mitigation for Automotive Radar Using Convolutional Neural Networks](https://mlsanity.com/paper/1906.10044/) (2019)
- [A Deep Learning Approach for Automotive Radar Interference Mitigation](https://mlsanity.com/paper/1903.06380/) (2019)
- [Deep Interference Mitigation and Denoising of Real-World FMCW Radar Signals](https://mlsanity.com/paper/2012.02529/) (2020)

## Built on by (most cited first, 7 of 7)

- [Interference Suppression Using Deep Learning: Current Approaches and Open Challenges](https://mlsanity.com/paper/2112.08988/) (2021)
- [A Survey of Deep Learning for Complex Speech Spectrograms](https://mlsanity.com/paper/2505.08694/) (2025)
- [Complex-valued Neural Networks -- Theory and Analysis](https://mlsanity.com/paper/2312.06087/) (2023)
- [End-to-End Training of Neural Networks for Automotive Radar Interference Mitigation](https://mlsanity.com/paper/2312.09790/) (2023)
- [Holographic Transformers for Complex-Valued Signal Processing: Integrating Phase Interference into Self-Attention](https://mlsanity.com/paper/2509.19331/) (2025)
- [Hybrid Real- and Complex-valued Neural Network Architecture](https://mlsanity.com/paper/2504.03497/) (2025)
- [Uncertainty-Driven Radar-Inertial Fusion for Instantaneous 3D Ego-Velocity Estimation](https://mlsanity.com/paper/2506.14294/) (2025)

## Abstract (arXiv)

Autonomous driving highly depends on capable sensors to perceive the environment and to deliver reliable information to the vehicles' control systems. To increase its robustness, a diversified set of sensors is used, including radar sensors. Radar is a vital contribution of sensory information, providing high resolution range as well as velocity measurements. The increased use of radar sensors in road traffic introduces new challenges. As the so far unregulated frequency band becomes increasingly crowded, radar sensors suffer from mutual interference between multiple radar sensors. This interference must be mitigated in order to ensure a high and consistent detection sensitivity. In this paper, we propose the use of Complex-Valued Convolutional Neural Networks (CVCNNs) to address the issue of mutual interference between radar sensors. We extend previously developed methods to the complex domain in order to process radar data according to its physical characteristics. This not only increases data efficiency, but also improves the conservation of phase information during filtering, which is crucial for further processing, such as angle estimation. Our experiments show, that the use of CVCNNs increases data efficiency, speeds up network training and substantially improves the conservation of phase information during interference removal.