TY - JOUR
T1 - Linear-Array-MIMO SAR Tomography
T2 - An Autofocus Approach for Time-Variant and 3-D Space-Variant Motion Errors
AU - Li, Linghao
AU - Ding, Zegang
AU - Wang, Yan
AU - Gao, Wenbin
AU - Liu, Minkun
AU - Zhang, Tianyi
AU - Tian, Weiming
AU - Zeng, Tao
N1 - Publisher Copyright:
© 1980-2012 IEEE.
PY - 2022
Y1 - 2022
N2 - 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.
AB - 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.
KW - 3-D imaging and autofocusing
KW - 3-D space-variant motion errors (3-D SVMEs)
KW - multiple-input-multiple-output (MIMO) synthetic aperture radar (SAR)
UR - http://www.scopus.com/inward/record.url?scp=85112563193&partnerID=8YFLogxK
U2 - 10.1109/TGRS.2021.3102072
DO - 10.1109/TGRS.2021.3102072
M3 - Article
AN - SCOPUS:85112563193
SN - 0196-2892
VL - 60
JO - IEEE Transactions on Geoscience and Remote Sensing
JF - IEEE Transactions on Geoscience and Remote Sensing
ER -