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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...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2018
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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. |
format | Online Article Text |
id | pubmed-5876736 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2018 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
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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