Volume 13 Issue 4
Aug.  2024
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Article Navigation >  Journal of Radars  > 2024  >  13(4): 761-776
LIN Yun, ZHAO Jiameng, WANG Yanping, et al. Closed space SAR multipath suppression method based on multi-angle dual-layer deviation measurement[J]. Journal of Radars, 2024, 13(4): 761–776. doi: 10.12000/JR24076
Citation: LIN Yun, ZHAO Jiameng, WANG Yanping, et al. Closed space SAR multipath suppression method based on multi-angle dual-layer deviation measurement[J]. Journal of Radars, 2024, 13(4): 761–776. doi: 10.12000/JR24076
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Closed Space SAR Multipath Suppression Method Based on Multi-angle Dual-layer Deviation Measurement

DOI: 10.12000/JR24076

  • Radar Monitoring Technology Laboratory, School of Information, North China University of Technology, Beijing 100144, China

Funds:  The National Natural Science Foundation of China (62131001, 62371005), The Innovation Team Building Support Program of the Beijing Municipal Education Commission (IDHT20190501)
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  • Corresponding author: WANG Yanping, wangyp@ncut.edu.cn
  • Received Date: 2024-04-28
  • Rev Recd Date: 2024-06-23
    • Available Online: 2024-06-27
  • Publish Date: 2024-07-10
    Abstract
  • Synthetic Aperture Radar (SAR) has the advantage of noncontact monitoring around the clock and is an important tool for closed space security monitoring. However, when SAR is employed in complex closed spaces, it is susceptible to multipath effects, resulting in a considerable number of virtual images in the image, which has a detrimental impact on interpretation. Existing methods require scene priors for multipath estimation or subaperture weighted fusion to suppress multipath; however, accurately distinguishing multipath virtual images from target images is challenging. This paper proposes a novel multi-angle dual-layer deviation measurement method that effectively distinguishes multipath virtual images from targets. The proposed method employs a large viewing angle difference to conduct multi-angle observation of the target scene, capitalizing on the fact that the position of the multipath virtual image varies with the observation angle, whereas the actual target position remains constant; this is followed by applying a dual-layer deviation measurement algorithm. The algorithm calculates the deviation between the sequence amplitude value and mean twice based on the sparsity of multipath in the multiangle sequence. The proposed method accurately detects and removes sparse and unstable multipath components, whereas the remaining stable components are averaged. This effectively suppresses multipath while retaining target information. Finally, the simulation and actual millimeter wave radar data processing verified the effectiveness of the proposed method.

     

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