Volume 11 Issue 1
Feb.  2022
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Article Navigation >  Journal of Radars  > 2022  >  11(1): 27-39
JIN Tian, SONG Yongkun, DAI Yongpeng, et al. UWB-HA4D-1.0: An ultra-wideband radar human activity 4D imaging dataset[J]. Journal of Radars, 2022, 11(1): 27–39. doi: 10.12000/JR22008
Citation: JIN Tian, SONG Yongkun, DAI Yongpeng, et al. UWB-HA4D-1.0: An ultra-wideband radar human activity 4D imaging dataset[J]. Journal of Radars, 2022, 11(1): 27–39. doi: 10.12000/JR22008
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UWB-HA4D-1.0: An Ultra-wideband Radar Human Activity 4D Imaging Dataset

doi: 10.12000/JR22008

  • College of Electronic Science and Technology, National University of Defense Technology, Changsha 410073, China

Funds:  The National Natural Science Foundation of China (61971430)
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  • Corresponding author: JIN Tian, tianjin@nudt.edu.cn
  • Received Date: 2022-01-09
  • Accepted Date: 2022-02-19
  • Rev Recd Date: 2022-02-16
    • Available Online: 2022-02-19
  • Publish Date: 2022-02-24
    Abstract
  • A radar human behavior perception system has penetration detection ability, which gives it a wide application prospect in the fields of security, rescue, medical treatment, and so on. Although the development of deep learning technology has promoted radar sensor research in human behavior perception, it requires more prompted dataset availability. This paper provides a four-dimensional imaging dataset of human activity using ultra-wideband radar, UWB-HA4D, which uses three-dimensional ultra-wideband multiple-input multiple-output radar as the detection sensor to capture the range-azimuth-height-time four-dimensional activity data of a human target. The dataset contains the activity data of 2757 groups for 11 human targets, including 10 common activities, such as walking, waving, and boxing. It also contains penetration and nonpenetration detection experimental scenarios. The radar system parameters, data generation process, data distribution, and other information of the dataset are introduced in detail herein. Meanwhile, several deep learning algorithms that are based on the PaddlePaddle framework and are widely used in the computer version field are applied to this dataset for human activity recognition. The experimental comparison results can be used to provide references for scholars and facilitate further investigation and research on this basis.

     

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