Radar Ghost Target Detection via Multimodal Transformers

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Ghost targets caused by inter-reflections are by design unavoidable in radar measurements, and it is challenging to distinguish these artifact detections from real ones. In this letter, we propose a novel approach to detect radar ghost targets by using LiDAR data as a reference. For this, we adopt a multimodal transformer network to learn interactions between points. We employ self-attention to exchange information between radar points, and local crossmodal attention to infuse information from surrounding LiDAR points. The key idea is that a ghost target should have higher semantic affinity with the reflected real target than the other ones. Extensive experiments on nuScenes [1] show that our method outperforms the baseline method on radar ghost target detection by a large margin.

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ISO 690WANG, Leichen, Simon GIEBENHAIN, Carsten ANKLAM, Bastian GOLDLÜCKE, 2021. Radar Ghost Target Detection via Multimodal Transformers. In: IEEE Robotics and Automation Letters. IEEE. 2021, 6(4), pp. 7758-7765. ISSN 2377-3774. eISSN 2377-3766. Available under: doi: 10.1109/LRA.2021.3100176
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@article{Wang2021Radar-54809,
  year={2021},
  doi={10.1109/LRA.2021.3100176},
  title={Radar Ghost Target Detection via Multimodal Transformers},
  number={4},
  volume={6},
  issn={2377-3774},
  journal={IEEE Robotics and Automation Letters},
  pages={7758--7765},
  author={Wang, Leichen and Giebenhain, Simon and Anklam, Carsten and Goldlücke, Bastian}
}
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