# Prior-Guided Deep Interference Mitigation for FMCW Radars

arXiv: https://arxiv.org/abs/2108.13023 · HTML: https://mlsanity.com/paper/2108.13023/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 30 Aug 2021.

## Built on by (most cited first, 4 of 4)

- [RadMamba: Efficient Human Activity Recognition through Radar-based Micro-Doppler-Oriented Mamba State-Space Model](https://mlsanity.com/paper/2504.12039/) (2025)
- [End-to-End Training of Neural Networks for Automotive Radar Interference Mitigation](https://mlsanity.com/paper/2312.09790/) (2023)
- [RIMformer: An End-to-End Transformer for FMCW Radar Interference Mitigation](https://mlsanity.com/paper/2407.11459/) (2024)
- [Neural-HAR: A Dimension-Gated CNN Accelerator for Real-Time Radar Human Activity Recognition](https://mlsanity.com/paper/2510.22772/) (2025)

## Abstract (arXiv)

A prior-guided deep learning (DL) based interference mitigation approach is proposed for frequency modulated continuous wave (FMCW) radars. In this paper, the interference mitigation problem is tackled as a regression problem. Considering the complex-valued nature of radar signals, the complex-valued convolutional neural network is utilized as an architecture for implementation, which is different from the conventional real-valued counterparts. Meanwhile, as the useful beat signals of FMCW radars and interferences exhibit different distributions in the time-frequency domain, this prior feature is exploited as a regularization term to avoid overfitting of the learned representation. The effectiveness and accuracy of our proposed complex-valued fully convolutional network (CV-FCN) based interference mitigation approach are verified and analyzed through both simulated and measured radar signals. Compared to the real-valued counterparts, the CV-FCN shows a better interference mitigation performance with a potential of half memory reduction in low Signal to Interference plus Noise Ratio (SINR) scenarios. Moreover, the CV-FCN trained using only simulated data can be directly utilized for interference mitigation in various measured radar signals and shows a superior generalization capability. Furthermore, by incorporating the prior feature, the CV-FCN trained on only 1/8 of the full data achieves comparable performance as that on the full dataset in low SINR scenarios, and the training procedure converges faster.