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LIU Hui, ZHAO Chunmeng, CHEN Pengfei, et al. Sparse matching filtering algorithm for tomographic SAR imaging[J]. Journal of Radars, in press. doi: 10.12000/JR25038
Citation: LIU Hui, ZHAO Chunmeng, CHEN Pengfei, et al. Sparse matching filtering algorithm for tomographic SAR imaging[J]. Journal of Radars, in press. doi: 10.12000/JR25038
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Sparse Matching Filtering Algorithm for Tomographic SAR Imaging

DOI: 10.12000/JR25038 CSTR: 32380.14.JR25038
  • 1.

    School of Intelligence Science and Technology, Beijing University of Civil Engineering and Architecture, Beijing 102616, China

  • 2.

    Beijing Key Laboratory of Super Intelligent Technology for Urban Architecture, Beijing University of Civil Engineering and Architecture, Beijing 102616, China

Funds:  The National Natural Science Foundation of China (61501019), China Higher Education Institution Industry-University-Research Innovation Fund (2022IT229)
More Information
  • Corresponding author: LIU Hui, liuhui@bucea.edu.cn
  • Received Date: 2025-02-25
  • Rev Recd Date: 2025-08-17
    • Available Online: 2025-08-19
    Abstract
  • Synthetic Aperture Radar Tomography (TomoSAR) has emerged as the primary technique for generating 3D SAR point clouds. In practice, ignoring the quadratic phase distribution in the elevation dimension causes defocusing artifacts due to inherent Fresnel diffraction in tomographic SAR processing. By comparing with optical diffraction theory, this paper identifies similar diffraction effects in the third dimension of SAR images and introduces a sparse matched filtering technique for tomographic focusing. Our method is based on deriving a sparse phase compensation factor to construct the matched filter. The proposed processing chain includes three key steps. First, a normalized sparse frequency profile in the tomographic dimension is constructed using the spatial geometric baseline of TomoSAR acquisitions. Next, we derive a frequency-domain sparse matched filter based on Fresnel integral properties incorporating system parameters such as wavelength, range, and aperture size. Finally, phase compensation is applied through the designed sparse filter, enabling elevation target detection using established sparse imaging techniques, including compressed sensing and likelihood ratio detection. This study employs airborne SAR data acquired by the Aerospace Information Research Institute, Chinese Academy of Sciences, to validate the proposed frequency-domain sparse matched filter. Experimental results demonstrate that our method effectively reduces tomographic defocusing artifacts caused by Fresnel diffraction, substantially improving the accuracy of both target localization and backscattering coefficient estimation.

     

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