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Open AccessArticle

Efficient Parameter Estimation for Sparse SAR Imaging Based on Complex Image and Azimuth-Range Decouple

by 1,2,3,*, 1,2,3, 1,2,3 and 1,2
1
Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China
2
School of Electronic, Electrical and Communication Engineering, University of Chinese Academy of Sciences, Beijing 101408, China
3
Key Laboratory of Technology in Geo-Spatial Information Processing and Application System, Institute of Electronics, Chinese Academy of Sciences, Beijing 100190, China
*
Author to whom correspondence should be addressed.
Sensors 2019, 19(20), 4549; https://doi.org/10.3390/s19204549
Received: 17 September 2019 / Revised: 13 October 2019 / Accepted: 17 October 2019 / Published: 19 October 2019
(This article belongs to the Section Remote Sensors)
Sparse signal processing theory has been applied to synthetic aperture radar (SAR) imaging. In compressive sensing (CS), the sparsity is usually considered as a known parameter. However, it is unknown practically. For many functions of CS, we need to know this parameter. Therefore, the estimation of sparsity is crucial for sparse SAR imaging. The sparsity is determined by the size of regularization parameter. Several methods have been presented for automatically estimating the regularization parameter, and have been applied to sparse SAR imaging. However, these methods are deduced based on an observation matrix, which will entail huge computational and memory costs. In this paper, to enhance the computational efficiency, an efficient adaptive parameter estimation method for sparse SAR imaging is proposed. The complex image-based sparse SAR imaging method only considers the threshold operation of the complex image, which can reduce the computational costs significantly. By utilizing this feature, the parameter is pre-estimated based on a complex image. In order to estimate the sparsity accurately, adaptive parameter estimation is then processed in the raw data domain, combining with the pre-estimated parameter and azimuth-range decouple operators. The proposed method can reduce the computational complexity from a quadratic square order to a linear logarithm order, which can be used in the large-scale scene. Simulated and Gaofen-3 SAR data processing results demonstrate the validity of the proposed method. View Full-Text
Keywords: sparse synthetic aperture radar (SAR) imaging; adaptive parameter estimation; compressive sensing (CS); L1 regularization; azimuth-range decouple; Gaofen-3 data sparse synthetic aperture radar (SAR) imaging; adaptive parameter estimation; compressive sensing (CS); L1 regularization; azimuth-range decouple; Gaofen-3 data
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MDPI and ACS Style

Liu, M.; Zhang, B.; Xu, Z.; Wu, Y. Efficient Parameter Estimation for Sparse SAR Imaging Based on Complex Image and Azimuth-Range Decouple. Sensors 2019, 19, 4549. https://doi.org/10.3390/s19204549

AMA Style

Liu M, Zhang B, Xu Z, Wu Y. Efficient Parameter Estimation for Sparse SAR Imaging Based on Complex Image and Azimuth-Range Decouple. Sensors. 2019; 19(20):4549. https://doi.org/10.3390/s19204549

Chicago/Turabian Style

Liu, Mingqian; Zhang, Bingchen; Xu, Zhongqiu; Wu, Yirong. 2019. "Efficient Parameter Estimation for Sparse SAR Imaging Based on Complex Image and Azimuth-Range Decouple" Sensors 19, no. 20: 4549. https://doi.org/10.3390/s19204549

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