TY - JOUR
T1 - 3-D ground-based imaging radar based on C-band cross-MIMO array and tensor compressive sensing
AU - Feng, Weike
AU - Friedt, Jean Michel
AU - Nico, Giovanni
AU - Sato, Motoyuki
N1 - Funding Information:
Manuscript received July 23, 2018; revised October 24, 2018; accepted March 15, 2019. Date of publication April 18, 2019; date of current version September 25, 2019. This work was supported by Japan Society for the Promotion of Science (JSPS) Grant-in-Aid for Scientific Research (A) under Grant 26249058. (Corresponding author: Weike Feng.) W. Feng is with the Graduate School of Environmental Studies, Tohoku University, Sendai 980-8572, Japan (e-mail: feng.weike.q4@dc.tohoku.ac.jp).
Publisher Copyright:
© 2019 IEEE.
PY - 2019/10
Y1 - 2019/10
N2 - —We designed a ground-based radar system with a C-band 2-D cross multiple input multiple output (MIMO) array for 3-D imaging and displacement estimation purposes. For this system, we developed a far-field pseudo-polar image format algorithm using pseudo-polar spherical coordinate. The use of a tensor compressive sensing technique allows to focus under-sampled raw data and to optimize the data acquisition time and memory usage. A novel algorithm, named as tensor-based iterative adaptive approach, is proposed for the effective and efficient reconstruction of sparse targets with a reduced level of sidelobes. Experimental results validate the designed radar system and the proposed algorithms.
AB - —We designed a ground-based radar system with a C-band 2-D cross multiple input multiple output (MIMO) array for 3-D imaging and displacement estimation purposes. For this system, we developed a far-field pseudo-polar image format algorithm using pseudo-polar spherical coordinate. The use of a tensor compressive sensing technique allows to focus under-sampled raw data and to optimize the data acquisition time and memory usage. A novel algorithm, named as tensor-based iterative adaptive approach, is proposed for the effective and efficient reconstruction of sparse targets with a reduced level of sidelobes. Experimental results validate the designed radar system and the proposed algorithms.
KW - 3-D imaging
KW - Compressive sensing (CS)
KW - Cross multiple input multiple output (MIMO)
KW - Iterative adaptive approach (IAA)
KW - Pseudo-polar spherical coordinate
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U2 - 10.1109/LGRS.2019.2906077
DO - 10.1109/LGRS.2019.2906077
M3 - Article
AN - SCOPUS:85070443529
VL - 16
SP - 1585
EP - 1589
JO - IEEE Geoscience and Remote Sensing Letters
JF - IEEE Geoscience and Remote Sensing Letters
SN - 1545-598X
IS - 10
M1 - 2906077
ER -