@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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    <dc:contributor>Giebenhain, Simon</dc:contributor>
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    <dc:creator>Wang, Leichen</dc:creator>
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    <dcterms:abstract xml:lang="eng">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.</dcterms:abstract>
    <dc:creator>Goldlücke, Bastian</dc:creator>
    <dcterms:title>Radar Ghost Target Detection via Multimodal Transformers</dcterms:title>
    <dc:creator>Anklam, Carsten</dc:creator>
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    <dc:contributor>Goldlücke, Bastian</dc:contributor>
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    <dc:creator>Giebenhain, Simon</dc:creator>
    <dc:contributor>Wang, Leichen</dc:contributor>
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