TY - JOUR
T1 - Baseline Design Method of GB-TomoArcSAR Based on Coding Optimization
AU - Tian, Weiming
AU - Yan, Kai
AU - Deng, Yunkai
AU - Xie, Xin
AU - Li, Yuanhao
N1 - Publisher Copyright:
© 2008-2012 IEEE.
PY - 2026
Y1 - 2026
N2 - Due to the limitation of lacking elevation resolution, ground-based interferometric radar suffers from serious layover problem when applied to building deformation monitoring. Ground-based tomographic arc-scanning synthetic aperture radar (GB-TomoArcSAR) enables tomography by controlling the antenna to scan multiple times in the horizontal plane of different elevation angles to form an arc-shaped synthetic aperture in the elevation direction. However, when GB-TomoArcSAR uses a baseline with uniformly distributed elevation angles, the excessive number of baseline samples lead to the low efficiency of tomography. To address this issue, this article proposes a tomographic baseline design method based on coding optimization. First, the sampling positions in the elevation direction are binary coded. Then, with the elevation resolution and peak side-lobe ratio of the antenna pointing pattern satisfying specific constraints, a quality evaluation model of the tomographic baseline is constructed by weighted summation of the sampling number and the Cramer Rao bound estimation accuracy in the elevation direction. Finally, the genetic algorithm is employed to iteratively solve the optimal coding to achieve sparse design of the tomographic baseline. Validation using both simulated and measured data demonstrates that the proposed method can reduce the number of elevation samples to 42% of the original before optimization while maintaining tomographic performance, substantially shortening the time for collecting measured data.
AB - Due to the limitation of lacking elevation resolution, ground-based interferometric radar suffers from serious layover problem when applied to building deformation monitoring. Ground-based tomographic arc-scanning synthetic aperture radar (GB-TomoArcSAR) enables tomography by controlling the antenna to scan multiple times in the horizontal plane of different elevation angles to form an arc-shaped synthetic aperture in the elevation direction. However, when GB-TomoArcSAR uses a baseline with uniformly distributed elevation angles, the excessive number of baseline samples lead to the low efficiency of tomography. To address this issue, this article proposes a tomographic baseline design method based on coding optimization. First, the sampling positions in the elevation direction are binary coded. Then, with the elevation resolution and peak side-lobe ratio of the antenna pointing pattern satisfying specific constraints, a quality evaluation model of the tomographic baseline is constructed by weighted summation of the sampling number and the Cramer Rao bound estimation accuracy in the elevation direction. Finally, the genetic algorithm is employed to iteratively solve the optimal coding to achieve sparse design of the tomographic baseline. Validation using both simulated and measured data demonstrates that the proposed method can reduce the number of elevation samples to 42% of the original before optimization while maintaining tomographic performance, substantially shortening the time for collecting measured data.
KW - Baseline optimization design
KW - coding optimization
KW - genetic algorithm
KW - ground-based tomographic arc-scanning synthetic aperture radar(GB-TomoArcSAR)
UR - https://www.scopus.com/pages/publications/105036666631
U2 - 10.1109/JSTARS.2026.3684386
DO - 10.1109/JSTARS.2026.3684386
M3 - Article
AN - SCOPUS:105036666631
SN - 1939-1404
VL - 19
SP - 13973
EP - 13985
JO - IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
JF - IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
ER -