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Penalized Maximum Likelihood Angular Super-Resolution Method for Scanning Radar Forward-Looking Imaging

Deconvolution provides an efficient technology to implement angular super-resolution for scanning radar forward-looking imaging. However, deconvolution is an ill-posed problem, of which the solution is not only sensitive to noise, but also would be easily deteriorate by the noise amplification when...

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Detalles Bibliográficos
Autores principales: Tan, Ke, Li, Wenchao, Zhang, Qian, Huang, Yulin, Wu, Junjie, Yang, Jianyu
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2018
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5876736/
https://www.ncbi.nlm.nih.gov/pubmed/29562722
http://dx.doi.org/10.3390/s18030912
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author Tan, Ke
Li, Wenchao
Zhang, Qian
Huang, Yulin
Wu, Junjie
Yang, Jianyu
author_facet Tan, Ke
Li, Wenchao
Zhang, Qian
Huang, Yulin
Wu, Junjie
Yang, Jianyu
author_sort Tan, Ke
collection PubMed
description Deconvolution provides an efficient technology to implement angular super-resolution for scanning radar forward-looking imaging. However, deconvolution is an ill-posed problem, of which the solution is not only sensitive to noise, but also would be easily deteriorate by the noise amplification when excessive iterations are conducted. In this paper, a penalized maximum likelihood angular super-resolution method is proposed to tackle these problems. Firstly, a new likelihood function is deduced by separately considering the noise in I and Q channels to enhance the accuracy of the noise modeling for radar imaging system. Afterwards, to conquer the noise amplification and maintain the resolving ability of the proposed method, a joint square-Laplace penalty is particularly formulated by making use of the outlier sensitivity property of square constraint as well as the sparse expression ability of Laplace distribution. Finally, in order to facilitate the engineering application of the proposed method, an accelerated iterative solution strategy is adopted to solve the obtained convex optimal problem. Experiments based on both synthetic data and real data demonstrate the effectiveness and superior performance of the proposed method.
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spelling pubmed-58767362018-04-09 Penalized Maximum Likelihood Angular Super-Resolution Method for Scanning Radar Forward-Looking Imaging Tan, Ke Li, Wenchao Zhang, Qian Huang, Yulin Wu, Junjie Yang, Jianyu Sensors (Basel) Article Deconvolution provides an efficient technology to implement angular super-resolution for scanning radar forward-looking imaging. However, deconvolution is an ill-posed problem, of which the solution is not only sensitive to noise, but also would be easily deteriorate by the noise amplification when excessive iterations are conducted. In this paper, a penalized maximum likelihood angular super-resolution method is proposed to tackle these problems. Firstly, a new likelihood function is deduced by separately considering the noise in I and Q channels to enhance the accuracy of the noise modeling for radar imaging system. Afterwards, to conquer the noise amplification and maintain the resolving ability of the proposed method, a joint square-Laplace penalty is particularly formulated by making use of the outlier sensitivity property of square constraint as well as the sparse expression ability of Laplace distribution. Finally, in order to facilitate the engineering application of the proposed method, an accelerated iterative solution strategy is adopted to solve the obtained convex optimal problem. Experiments based on both synthetic data and real data demonstrate the effectiveness and superior performance of the proposed method. MDPI 2018-03-19 /pmc/articles/PMC5876736/ /pubmed/29562722 http://dx.doi.org/10.3390/s18030912 Text en © 2018 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Tan, Ke
Li, Wenchao
Zhang, Qian
Huang, Yulin
Wu, Junjie
Yang, Jianyu
Penalized Maximum Likelihood Angular Super-Resolution Method for Scanning Radar Forward-Looking Imaging
title Penalized Maximum Likelihood Angular Super-Resolution Method for Scanning Radar Forward-Looking Imaging
title_full Penalized Maximum Likelihood Angular Super-Resolution Method for Scanning Radar Forward-Looking Imaging
title_fullStr Penalized Maximum Likelihood Angular Super-Resolution Method for Scanning Radar Forward-Looking Imaging
title_full_unstemmed Penalized Maximum Likelihood Angular Super-Resolution Method for Scanning Radar Forward-Looking Imaging
title_short Penalized Maximum Likelihood Angular Super-Resolution Method for Scanning Radar Forward-Looking Imaging
title_sort penalized maximum likelihood angular super-resolution method for scanning radar forward-looking imaging
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5876736/
https://www.ncbi.nlm.nih.gov/pubmed/29562722
http://dx.doi.org/10.3390/s18030912
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