# Resource-efficient Deep Neural Networks for Automotive Radar Interference Mitigation

arXiv: https://arxiv.org/abs/2201.10360 · HTML: https://mlsanity.com/paper/2201.10360/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 19 of its 40 references that are in the map.
- First posted on arXiv 25 Jan 2022.

## Builds on (most cited first, 19 of 19)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Categorical Reparameterization with Gumbel-Softmax](https://mlsanity.com/paper/1611.01144/) (2016)
- [Learning both Weights and Connections for Efficient Neural Networks](https://mlsanity.com/paper/1506.02626/) (2015)
- [The Concrete Distribution: A Continuous Relaxation of Discrete Random Variables](https://mlsanity.com/paper/1611.00712/) (2016)
- [ProxylessNAS: Direct Neural Architecture Search on Target Task and Hardware](https://mlsanity.com/paper/1812.00332/) (2018)
- [Learned Step Size Quantization](https://mlsanity.com/paper/1902.08153/) (2019)
- [Variational Dropout and the Local Reparameterization Trick](https://mlsanity.com/paper/1506.02557/) (2015)
- [Probabilistic Backpropagation for Scalable Learning of Bayesian Neural Networks](https://mlsanity.com/paper/1502.05336/) (2015)
- [Trained Quantization Thresholds for Accurate and Efficient Fixed-Point Inference of Deep Neural Networks](https://mlsanity.com/paper/1903.08066/) (2019)
- [Learning Discrete Weights Using the Local Reparameterization Trick](https://mlsanity.com/paper/1710.07739/) (2017)
- [Probabilistic Binary Neural Networks](https://mlsanity.com/paper/1809.03368/) (2018)
- [Resource-Efficient Neural Networks for Embedded Systems](https://mlsanity.com/paper/2001.03048/) (2020)
- [Mixed Precision DNNs: All you need is a good parametrization](https://mlsanity.com/paper/1905.11452/) (2019)
- [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)
- [Automotive Radar Interference Mitigation Using Adaptive Noise Canceller](https://mlsanity.com/paper/1911.06372/) (2019)

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

- [Radar-Camera Fusion for Object Detection and Semantic Segmentation in Autonomous Driving: A Comprehensive Review](https://mlsanity.com/paper/2304.10410/) (2023)
- [Balance is Essence: Accelerating Sparse Training via Adaptive Gradient Correction](https://mlsanity.com/paper/2301.03573/) (2023)
- [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)
- [Angle-Equivariant Convolutional Neural Networks for Interference Mitigation in Automotive Radar](https://mlsanity.com/paper/2401.05385/) (2023)
- [Temporal Convolutional Autoencoder for Interference Mitigation in FMCW Radar Altimeters](https://mlsanity.com/paper/2505.22783/) (2025)

## Abstract (arXiv)

Radar sensors are crucial for environment perception of driver assistance systems as well as autonomous vehicles. With a rising number of radar sensors and the so far unregulated automotive radar frequency band, mutual interference is inevitable and must be dealt with. Algorithms and models operating on radar data are required to run the early processing steps on specialized radar sensor hardware. This specialized hardware typically has strict resource-constraints, i.e. a low memory capacity and low computational power. Convolutional Neural Network (CNN)-based approaches for denoising and interference mitigation yield promising results for radar processing in terms of performance. Regarding resource-constraints, however, CNNs typically exceed the hardware's capacities by far. In this paper we investigate quantization techniques for CNN-based denoising and interference mitigation of radar signals. We analyze the quantization of (i) weights and (ii) activations of different CNN-based model architectures. This quantization results in reduced memory requirements for model storage and during inference. We compare models with fixed and learned bit-widths and contrast two different methodologies for training quantized CNNs, i.e. the straight-through gradient estimator and training distributions over discrete weights. We illustrate the importance of structurally small real-valued base models for quantization and show that learned bit-widths yield the smallest models. We achieve a memory reduction of around 80% compared to the real-valued baseline. Due to practical reasons, however, we recommend the use of 8 bits for weights and activations, which results in models that require only 0.2 megabytes of memory.