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Image-Based RCS Estimation from Near-Field Data

This paper deals with the problem of estimating the RCS from near-field data by image-based approaches. In particular, a rigorous focusing procedure based on a weighted adjoint scheme, which is also applicable to an arbitrary measurement curve, is developed. The developed formalism allows us to addr...

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Detalles Bibliográficos
Autores principales: Rajvanshi, Tushar, Maisto, Maria Antonia, Dell’Aversano, Angela, Solimene, Raffaele
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2019
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8320939/
https://www.ncbi.nlm.nih.gov/pubmed/34460499
http://dx.doi.org/10.3390/jimaging5060061
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author Rajvanshi, Tushar
Maisto, Maria Antonia
Dell’Aversano, Angela
Solimene, Raffaele
author_facet Rajvanshi, Tushar
Maisto, Maria Antonia
Dell’Aversano, Angela
Solimene, Raffaele
author_sort Rajvanshi, Tushar
collection PubMed
description This paper deals with the problem of estimating the RCS from near-field data by image-based approaches. In particular, a rigorous focusing procedure based on a weighted adjoint scheme, which is also applicable to an arbitrary measurement curve, is developed. The developed formalism allows us to address the important question concerning the need to employ a multi-frequency configuration to estimate the RCS. Accordingly, it is shown that if RCS is required at a given frequency, then the target image obtained solely at such a frequency can be exploited provided that the spatial truncation arising from the size of the investigated area is properly taken into account.
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spelling pubmed-83209392021-08-26 Image-Based RCS Estimation from Near-Field Data Rajvanshi, Tushar Maisto, Maria Antonia Dell’Aversano, Angela Solimene, Raffaele J Imaging Article This paper deals with the problem of estimating the RCS from near-field data by image-based approaches. In particular, a rigorous focusing procedure based on a weighted adjoint scheme, which is also applicable to an arbitrary measurement curve, is developed. The developed formalism allows us to address the important question concerning the need to employ a multi-frequency configuration to estimate the RCS. Accordingly, it is shown that if RCS is required at a given frequency, then the target image obtained solely at such a frequency can be exploited provided that the spatial truncation arising from the size of the investigated area is properly taken into account. MDPI 2019-06-17 /pmc/articles/PMC8320939/ /pubmed/34460499 http://dx.doi.org/10.3390/jimaging5060061 Text en © 2019 by the authors. https://creativecommons.org/licenses/by/4.0/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/ (https://creativecommons.org/licenses/by/4.0/) ).
spellingShingle Article
Rajvanshi, Tushar
Maisto, Maria Antonia
Dell’Aversano, Angela
Solimene, Raffaele
Image-Based RCS Estimation from Near-Field Data
title Image-Based RCS Estimation from Near-Field Data
title_full Image-Based RCS Estimation from Near-Field Data
title_fullStr Image-Based RCS Estimation from Near-Field Data
title_full_unstemmed Image-Based RCS Estimation from Near-Field Data
title_short Image-Based RCS Estimation from Near-Field Data
title_sort image-based rcs estimation from near-field data
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8320939/
https://www.ncbi.nlm.nih.gov/pubmed/34460499
http://dx.doi.org/10.3390/jimaging5060061
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