RIMformer: An End-to-End Transformer for FMCW Radar Interference Mitigation
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arXiv
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| Hauptverfasser: | , , , , , |
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| Format: | Preprint |
| Veröffentlicht: |
2024
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| _version_ | 1866914874335952896 |
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| author | Zhang, Ziang Chen, Guangzhi Weng, Youlong Yang, Shunchuan Jia, Zhiyu Chen, Jingxuan |
| author_facet | Zhang, Ziang Chen, Guangzhi Weng, Youlong Yang, Shunchuan Jia, Zhiyu Chen, Jingxuan |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2407_11459 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | RIMformer: An End-to-End Transformer for FMCW Radar Interference Mitigation Zhang, Ziang Chen, Guangzhi Weng, Youlong Yang, Shunchuan Jia, Zhiyu Chen, Jingxuan Signal Processing Machine Learning 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. |
| title | RIMformer: An End-to-End Transformer for FMCW Radar Interference Mitigation |
| topic | Signal Processing Machine Learning |
| url | https://arxiv.org/abs/2407.11459 |