Research Article
Downward-Looking Sparse Linear Array Synthetic Aperture Radar 3-D Imaging Method Based on CS-MUSIC
@INPROCEEDINGS{10.1007/978-3-319-73447-7_19, author={Fu-fei Gu and Le Kang and Jiang Zhao and Yin Zhang and Qun Zhang}, title={Downward-Looking Sparse Linear Array Synthetic Aperture Radar 3-D Imaging Method Based on CS-MUSIC}, proceedings={Machine Learning and Intelligent Communications. Second International Conference, MLICOM 2017, Weihai, China, August 5-6, 2017, Proceedings, Part II}, proceedings_a={MLICOM}, year={2018}, month={2}, keywords={Three-dimensional synthetic aperture radar Sparse linear array Compressive sensing Multiple-signal-classification Multiple Measurement Vectors}, doi={10.1007/978-3-319-73447-7_19} }- Fu-fei Gu
Le Kang
Jiang Zhao
Yin Zhang
Qun Zhang
Year: 2018
Downward-Looking Sparse Linear Array Synthetic Aperture Radar 3-D Imaging Method Based on CS-MUSIC
MLICOM
Springer
DOI: 10.1007/978-3-319-73447-7_19
Abstract
In this paper, a three-dimensional imaging method for sparse multiple input multiple output (MIMO) synthetic aperture radar (SAR) is proposed. Due to the limitation of the antenna array length in DLSLA 3-D SAR, the cross-track resolution is poor than the resolution in high and along-track direction. To obtain high resolution in cross-track domain, the multiple signal classification (MUSIC) algorithm is introduced into the imaging problem. However, the MUSIC invalid under the condition of less snapshot numbers and presence of coherent sources, which may be caused by data missing or sparse sampling in practice. To overcome these limitations, after the preprocessing such as the range and along-track imaging with ordinary Nyquist based methods, the motion compensation and the quadratic phase compensation, this paper transform the process of cross-track direction into a multiple measurement vectors (MMV) model and applies compressive multiple signal classification (CS-MUSIC) algorithm rather than the conventional method or MUSIC algorithm. Based on CS-MUSIC algorithm, imaging result of high resolution with less snapshot numbers. Compared with the CS-based method, the proposed approach can obtain a better performance of anti-noise. The simulated results confirm the effect of the method and show that it can improve the imaging quality.