Bias Analysis and Correction for Ill-Posed Inversion Problem with Sparsity Regularization Based on <i>L</i><sub>1</sub> Norm for Azimuth Super-Resolution of Radar Forward-Looking Imaging

Bias Analysis and Correction for Ill-Posed Inversion Problem with Sparsity Regularization Based on <i>L</i><sub>1</sub> Norm for Azimuth Super-Resolution of Radar Forward-Looking Imaging

The sparsity regularization based on the <i>L</i><sub>1</sub> norm can significantly stabilize the solution of the ill-posed sparsity inversion problem, e.g., azimuth super-resolution of radar forward-looking imaging, which can effectively suppress the noise and reduce the bl...

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Bibliographic Details
Main Authors:	Jie Han, Songlin Zhang, Shouzhu Zheng, Minghua Wang, Haiyong Ding, Qingyun Yan
Format:	Article
Language:	English
Published:	MDPI AG 2022-11-01
Series:	Remote Sensing
Subjects:	<i>L</i><sub>1</sub> norm bias correction deconvolution sparsity regularization azimuth super-resolution
Online Access:	https://www.mdpi.com/2072-4292/14/22/5792

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