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LI Hang, GUO Qichang, BU Xiangxi, et al. Time-Varying baseline estimation algorithm for small uavs-borne distributed tomosar[J]. Journal of Radars, in press. doi: 10.12000/JR25268
Citation: LI Hang, GUO Qichang, BU Xiangxi, et al. Time-Varying baseline estimation algorithm for small uavs-borne distributed tomosar[J]. Journal of Radars, in press. doi: 10.12000/JR25268
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Time-Varying Baseline Estimation Algorithm for Small UAVs-Borne Distributed TomoSAR

DOI: 10.12000/JR25268 CSTR: 32380.14.JR25268
  • 1.

    Aerospace Information Research Institute, Chinese Academy of Science, Beijing 100190, China

  • 2.

    School of Electronic, Electrical and Communication Engineering, University of Chinese Academy of Sciences, Beijing 100049, China

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    Abstract
  • Small unmanned aerial vehicle (UAV)–borne distributed tomographic synthetic aperture radar (TomoSAR) systems exhibit remarkable residual time-varying baseline errors due to the limited precision of the position and orientation system on small UAV platforms. These errors critically degrade the performance of three-dimensional (3D) target reconstruction. Compared with airborne repeat-pass 3D synthetic aperture radar (SAR), distributed TomoSAR mounted on small UAVs imposes stricter compensation accuracy requirements for time-varying baseline errors because of the altitude constraints of the carrying platform. Under the conditions of low signal-to-noise ratio and substantial time-varying baseline errors, existing estimation methods often fail to provide stable and reliable results. In this paper, a two-step time-varying baseline error estimation method based on image azimuth displacement is proposed. The method sequentially estimates the low-frequency component through the co-registration of the master and slave images and the high-frequency component using a multisquint algorithm. Iterative refinement is applied to enhance estimation accuracy. The experimental results obtained from real C-band small UAV-borne distributed TomoSAR data demonstrate that, compared with the enhanced multisquint processing method, the proposed method considerably reduces the root mean square of differential interferometric phases across most channels, thereby effectively improving interchannel coherence. In addition, the elevation-direction standard deviation of the reconstructed point cloud is reduced from 5.16 to 1.33 m, and the height reconstruction error of building targets is less than 0.5 m, validating the effectiveness and superiority of the proposed method.

     

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