# RIMformer: An End-to-End Transformer for FMCW Radar Interference Mitigation

arXiv: https://arxiv.org/abs/2407.11459 · HTML: https://mlsanity.com/paper/2407.11459/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 13 of its 45 references that are in the map.
- First posted on arXiv 16 Jul 2024.

## Builds on (most cited first, 13 of 13)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [SGDR: Stochastic Gradient Descent with Warm Restarts](https://mlsanity.com/paper/1608.03983/) (2016)
- [Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification](https://mlsanity.com/paper/1502.01852/) (2015)
- [Conformer: Convolution-augmented Transformer for Speech Recognition](https://mlsanity.com/paper/2005.08100/) (2020)
- [Self-Attention with Relative Position Representations](https://mlsanity.com/paper/1803.02155/) (2018)
- [Language Modeling with Gated Convolutional Networks](https://mlsanity.com/paper/1612.08083/) (2016)
- [Resource-efficient Deep Neural Networks for Automotive Radar Interference Mitigation](https://mlsanity.com/paper/2201.10360/) (2022)
- [Automotive Radar Interference Mitigation Using Adaptive Noise Canceller](https://mlsanity.com/paper/1911.06372/) (2019)
- [Prior-Guided Deep Interference Mitigation for FMCW Radars](https://mlsanity.com/paper/2108.13023/) (2021)
- [Interference Mitigation for FMCW Radar With Sparse and Low-Rank Hankel Matrix Decomposition](https://mlsanity.com/paper/2106.06748/) (2021)
- [Smoothed Phase-Coded FMCW: Waveform Properties and Transceiver Architecture](https://mlsanity.com/paper/2203.07508/) (2022)

## Built on by (most cited first, 1 of 1)

- [Rad-R: A Raw-ADC Radar Dataset and Capture-Invariant SSM for Hardware-Fault Diagnosis](https://mlsanity.com/paper/2608.30896/) (2026)

## Abstract (arXiv)

Frequency-modulated continuous-wave (FMCW) radar plays a pivotal role in the field of remote sensing. The increasing degree of FMCW radar deployment has increased the mutual interference, which weakens the detection capabilities of radars and threatens reliability and safety of systems. In this paper, a novel FMCW radar interference mitigation (RIM) method, termed as RIMformer, is proposed by using an end-to-end Transformer-based structure. In the RIMformer, a dual multi-head self-attention mechanism is proposed to capture the correlations among the distinct distance elements of intermediate frequency (IF) signals. Additionally, an improved convolutional block is integrated to harness the power of convolution for extracting local features. The architecture is designed to process time-domain IF signals in an end-to-end manner, thereby avoiding the need for additional manual data processing steps. The improved decoder structure ensures the parallelization of the network to increase its computational efficiency. Simulation and measurement experiments are carried out to validate the accuracy and effectiveness of the proposed method. The results show that the proposed RIMformer can effectively mitigate interference and restore the target signals.