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.
The authors would like to express thanks for the support of the National Natural Science Foundation of China (Grant No. 61501498, 61471386).
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© 2018 ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering
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Gu, Ff., Kang, L., Zhao, J., Zhang, Y., Zhang, Q. (2018). Downward-Looking Sparse Linear Array Synthetic Aperture Radar 3-D Imaging Method Based on CS-MUSIC. In: Gu, X., Liu, G., Li, B. (eds) Machine Learning and Intelligent Communications. MLICOM 2017. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 227. Springer, Cham. https://doi.org/10.1007/978-3-319-73447-7_19
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