Linear-Array-MIMO SAR Tomography: An Autofocus Approach for Time-Variant and 3-D Space-Variant Motion Errors

Linghao Li, Zegang Ding, Yan Wang*, Wenbin Gao, Minkun Liu, Tianyi Zhang, Weiming Tian, Tao Zeng

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

7 Citations (Scopus)
Plum Print visual indicator of research metrics
  • Citations
    • Citation Indexes: 7
  • Captures
    • Readers: 1
see details

Abstract

Linear-array multiple-input-multiple-output (LA-MIMO) synthetic aperture radar (SAR) can obtain 3-D radar images by only one pass. However, it is sensitive to time-variant measurement errors of curved track and time-variant attitude angles, meaning that autofocus processing for the LA-MIMO SAR tomography is necessary. The existing autofocus methods cannot be used to estimate the time-variant and 3-D space-variant motion errors (3-D SVME) of the LA-MIMO SAR. To solve this problem, a new autofocus approach based on multiple local autofocusing and the LA-MIMO SAR time-variant motion error estimation is proposed. First, the local motion error estimation based on the fast local spectral analysis (SPECAN) 3-D imaging and the maximum contrast optimization 2-D local autofocusing is performed to estimate the local time-variant motion errors. Then, based on the linear-array motion error model, the time-variant 3-D trajectory deviations of the array center and attitude angles are estimated by the weighted least square estimation (WLSE) to solve the 3-D SVMEs. Last, the 3-D fast factorized backprojection (FFBP) is performed to obtain the well-focused 3-D image of the whole beam. The proposed approach has been applied for the tomography of a new crawler-type unmanned-ground-vehicle (UGV) LA-MIMO SAR. Both the simulation and real data experiments verify the effectiveness of the proposed approach.

Original languageEnglish
JournalIEEE Transactions on Geoscience and Remote Sensing
Volume60
DOIs
Publication statusPublished - 2022

Keywords

  • 3-D imaging and autofocusing
  • 3-D space-variant motion errors (3-D SVMEs)
  • multiple-input-multiple-output (MIMO) synthetic aperture radar (SAR)

Fingerprint

Dive into the research topics of 'Linear-Array-MIMO SAR Tomography: An Autofocus Approach for Time-Variant and 3-D Space-Variant Motion Errors'. Together they form a unique fingerprint.

Cite this

Li, L., Ding, Z., Wang, Y., Gao, W., Liu, M., Zhang, T., Tian, W., & Zeng, T. (2022). Linear-Array-MIMO SAR Tomography: An Autofocus Approach for Time-Variant and 3-D Space-Variant Motion Errors. IEEE Transactions on Geoscience and Remote Sensing, 60. https://doi.org/10.1109/TGRS.2021.3102072