Volume 13 Issue 4
Aug.  2024
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Article Navigation >  Journal of Radars  > 2024  >  13(4): 731-746
YANG Xiaopeng, MA Zhongjie, ZHONG Shichao, et al. Trajectory planning method for UAV-through-the-wall 3D SAR based on a genetic algorithm[J]. Journal of Radars, 2024, 13(4): 731–746. doi: 10.12000/JR24068
Citation: YANG Xiaopeng, MA Zhongjie, ZHONG Shichao, et al. Trajectory planning method for UAV-through-the-wall 3D SAR based on a genetic algorithm[J]. Journal of Radars, 2024, 13(4): 731–746. doi: 10.12000/JR24068
shu

Trajectory Planning Method for UAV-through-the-wall 3D SAR Based on a Genetic Algorithm

DOI: 10.12000/JR24068 CSTR: 32380.14.JR24068
  • 1.

    School of Information and Electronics, Beijing Institute of Technology, Beijing 100081, China

  • 2.

    Yangtze Delta Region Academy of Beijing Institute of Technology, Jiaxing 314000, China

Funds:  The National Natural Science Foundation of China (62101042)
More Information
  • Corresponding author: ZHONG Shichao, zhongshichao16@bit.edu.cn
  • Received Date: 2024-04-19
  • Rev Recd Date: 2024-06-13
    • Available Online: 2024-06-19
  • Publish Date: 2024-07-02
    Abstract
  • Due to height limitations, the traditional handheld or vehicle-mounted Through-the-Wall Radar (TWR) cannot provide the perspective imaging of internal targets in urban high-rise buildings. Unmanned Aerial Vehicle-TWR (UAV-TWR) offers flexibility, efficiency, convenience, and no height limitations, allowing for large-scale three-Dimensional (3D) penetration detection of urban high-rise buildings. While the multibaseline scanning mode is widely used in 3D tomographic Synthetic Aperture Radar (SAR) imaging to provide resolution in the altitude direction, it often suffers from the grating lobe problem owing to under-sampling in the altitude spatial domain. Therefore, this paper proposes a trajectory planning algorithm for UAV-through-the-wall 3D SAR imaging based on a genetic algorithm to address this issue. By nonuniformizing flight trajectories, the periodic radar echo energy superposition is weakened, thereby suppressing grating lobes to achieve better imaging quality. The proposed algorithm combines the inherent relationship between the flight distance and TWR imaging quality and establishes a cost function for UAV-TWR trajectory planning. We use the genetic algorithm to encode genes for three typical flight trajectory control points and optimize the population and individuals through gene hybridization and mutation. The optimal flight trajectory for each of the three flight modes is selected by minimizing the cost function. Compared with the traditional equidistant multibaseline flight mode, the imaging results from simulations and measured data show that the proposed algorithm significantly suppresses the grating lobe effect of targets. In addition, oblique UAV flight trajectories are significantly shortened, improving the efficiency of through-the-wall SAR imaging.

     

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    • References(26)
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      沈阳化工大学材料科学与工程学院 沈阳 110142

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