3-D ground-based imaging radar based on C-band cross-MIMO array and tensor compressive sensing

Weike Feng; Jean Michel Friedt; Giovanni Nico; Motoyuki Sato

doi:10.1109/LGRS.2019.2906077

3-D ground-based imaging radar based on C-band cross-MIMO array and tensor compressive sensing

Weike Feng, Jean Michel Friedt, Giovanni Nico, Motoyuki Sato

Department of Basic Studies

Research output: Contribution to journal › Article › peer-review

2 Citations (Scopus)

Abstract

—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.

Original language	English
Article number	2906077
Pages (from-to)	1585-1589
Number of pages	5
Journal	IEEE Geoscience and Remote Sensing Letters
Volume	16
Issue number	10
DOIs	https://doi.org/10.1109/LGRS.2019.2906077
Publication status	Published - 2019 Oct

Keywords

3-D imaging
Compressive sensing (CS)
Cross multiple input multiple output (MIMO)
Iterative adaptive approach (IAA)
Pseudo-polar spherical coordinate

ASJC Scopus subject areas

Geotechnical Engineering and Engineering Geology
Electrical and Electronic Engineering

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abstract = "—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.",

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AU - Sato, Motoyuki

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