ISAR Dataset for the Recognition of Conical Targets with Micro-Motion

Abstract In recent years, the recognition of ballistic micro-motion targets based on deep learning has been extensively studied. However, currently, there are no publicly available datasets; all datasets come from simulations conducted by researchers themselves. In this study, it was found that even...

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Main Authors: Zhichen Zhao, Degui Yang, Xing Wang, Jianxuan Xu
Format: Article
Language:English
Published: Nature Portfolio 2025-06-01
Series:Scientific Data
Online Access:https://doi.org/10.1038/s41597-025-05193-4
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author Zhichen Zhao
Degui Yang
Xing Wang
Jianxuan Xu
author_facet Zhichen Zhao
Degui Yang
Xing Wang
Jianxuan Xu
author_sort Zhichen Zhao
collection DOAJ
description Abstract In recent years, the recognition of ballistic micro-motion targets based on deep learning has been extensively studied. However, currently, there are no publicly available datasets; all datasets come from simulations conducted by researchers themselves. In this study, it was found that even when the motion parameters and model are kept the same, the details of the electromagnetic simulation method have a significant impact on the data. Therefore, there is an urgent need for a publicly available dataset to evaluate the performance of different methods. The ISAR Micro-Motion Dataset (IMD) is a simulated radar echo dataset based on the working principles of fully polarimetric ISAR. It consists of two components: aspect angle sequence data and static electric field data of the target. This paper presents a unified process for generating target radar echoes and discusses how various details can impact the results.
format Article
id doaj-art-98402d33ef8d4ca798d8a8c8a1d45849
institution Kabale University
issn 2052-4463
language English
publishDate 2025-06-01
publisher Nature Portfolio
record_format Article
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spelling doaj-art-98402d33ef8d4ca798d8a8c8a1d458492025-08-20T03:45:11ZengNature PortfolioScientific Data2052-44632025-06-0112111210.1038/s41597-025-05193-4ISAR Dataset for the Recognition of Conical Targets with Micro-MotionZhichen Zhao0Degui Yang1Xing Wang2Jianxuan Xu3School of Automation, Central South UniversitySchool of Automation, Central South UniversitySchool of Automation, Central South UniversitySchool of Computing, Macquarie UniversityAbstract In recent years, the recognition of ballistic micro-motion targets based on deep learning has been extensively studied. However, currently, there are no publicly available datasets; all datasets come from simulations conducted by researchers themselves. In this study, it was found that even when the motion parameters and model are kept the same, the details of the electromagnetic simulation method have a significant impact on the data. Therefore, there is an urgent need for a publicly available dataset to evaluate the performance of different methods. The ISAR Micro-Motion Dataset (IMD) is a simulated radar echo dataset based on the working principles of fully polarimetric ISAR. It consists of two components: aspect angle sequence data and static electric field data of the target. This paper presents a unified process for generating target radar echoes and discusses how various details can impact the results.https://doi.org/10.1038/s41597-025-05193-4
spellingShingle Zhichen Zhao
Degui Yang
Xing Wang
Jianxuan Xu
ISAR Dataset for the Recognition of Conical Targets with Micro-Motion
Scientific Data
title ISAR Dataset for the Recognition of Conical Targets with Micro-Motion
title_full ISAR Dataset for the Recognition of Conical Targets with Micro-Motion
title_fullStr ISAR Dataset for the Recognition of Conical Targets with Micro-Motion
title_full_unstemmed ISAR Dataset for the Recognition of Conical Targets with Micro-Motion
title_short ISAR Dataset for the Recognition of Conical Targets with Micro-Motion
title_sort isar dataset for the recognition of conical targets with micro motion
url https://doi.org/10.1038/s41597-025-05193-4
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AT deguiyang isardatasetfortherecognitionofconicaltargetswithmicromotion
AT xingwang isardatasetfortherecognitionofconicaltargetswithmicromotion
AT jianxuanxu isardatasetfortherecognitionofconicaltargetswithmicromotion