Linear-array-MIMO SAR tomography: An autofocus approach for time-variant and 3-D space-variant motion errors
L Li, Z Ding, Y Wang, W Gao, M Liu… - … on Geoscience and …, 2021 - ieeexplore.ieee.org
L Li, Z Ding, Y Wang, W Gao, M Liu, T Zhang, W Tian, T Zeng
IEEE Transactions on Geoscience and Remote Sensing, 2021•ieeexplore.ieee.orgLinear-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 …
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 …
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.
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