# End-to-End Training of Neural Networks for Automotive Radar Interference Mitigation

arXiv: https://arxiv.org/abs/2312.09790 · HTML: https://mlsanity.com/paper/2312.09790/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 27 references that are in the map.
- First posted on arXiv 15 Dec 2023.

## Builds on (most cited first, 12 of 12)

- [Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks](https://mlsanity.com/paper/1506.01497/) (2015)
- [SSD: Single Shot MultiBox Detector](https://mlsanity.com/paper/1512.02325/) (2015)
- [Estimating or Propagating Gradients Through Stochastic Neurons for Conditional Computation](https://mlsanity.com/paper/1308.3432/) (2013)
- [A survey of loss functions for semantic segmentation](https://mlsanity.com/paper/2006.14822/) (2020)
- [RAMP-CNN: A Novel Neural Network for Enhanced Automotive Radar Object Recognition](https://mlsanity.com/paper/2011.08981/) (2020)
- [Deep Radar Detector](https://mlsanity.com/paper/1906.12187/) (2019)
- [Fully Convolutional Neural Networks for Automotive Radar Interference Mitigation](https://mlsanity.com/paper/2007.11102/) (2020)
- [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)
- [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)
- [Prior-Guided Deep Interference Mitigation for FMCW Radars](https://mlsanity.com/paper/2108.13023/) (2021)

## Built on by (most cited first, 2 of 2)

- [Learning to Rank for Multiple Retrieval-Augmented Models through Iterative Utility Maximization](https://mlsanity.com/paper/2410.09942/) (2024)
- [Angle-Equivariant Convolutional Neural Networks for Interference Mitigation in Automotive Radar](https://mlsanity.com/paper/2401.05385/) (2023)

## Abstract (arXiv)

In this paper we propose a new method for training neural networks (NNs) for frequency modulated continuous wave (FMCW) radar mutual interference mitigation. Instead of training NNs to regress from interfered to clean radar signals as in previous work, we train NNs directly on object detection maps. We do so by performing a continuous relaxation of the cell-averaging constant false alarm rate (CA-CFAR) peak detector, which is a well-established algorithm for object detection using radar. With this new training objective we are able to increase object detection performance by a large margin. Furthermore, we introduce separable convolution kernels to strongly reduce the number of parameters and computational complexity of convolutional NN architectures for radar applications. We validate our contributions with experiments on real-world measurement data and compare them against signal processing interference mitigation methods.