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Space Target Classification Improvement by Generating Micro-Doppler Signatures Considering Incident Angle
Classifying space targets from debris is critical for radar resource management as well as rapid response during the mid-course phase of space target flight. Due to advances in deep learning techniques, various approaches have been studied to classify space targets by using micro-Doppler signatures....
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8877989/ https://www.ncbi.nlm.nih.gov/pubmed/35214555 http://dx.doi.org/10.3390/s22041653 |
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author | Lee, Jae-In Kim, Nammon Min, Sawon Kim, Jeongwoo Jeong, Dae-Kyo Seo, Dong-Wook |
author_facet | Lee, Jae-In Kim, Nammon Min, Sawon Kim, Jeongwoo Jeong, Dae-Kyo Seo, Dong-Wook |
author_sort | Lee, Jae-In |
collection | PubMed |
description | Classifying space targets from debris is critical for radar resource management as well as rapid response during the mid-course phase of space target flight. Due to advances in deep learning techniques, various approaches have been studied to classify space targets by using micro-Doppler signatures. Previous studies have only used micro-Doppler signatures such as spectrogram and cadence velocity diagram (CVD), but in this paper, we propose a method to generate micro-Doppler signatures taking into account the relative incident angle that a radar can obtain during the target tracking process. The AlexNet and ResNet-18 networks, which are representative convolutional neural network architectures, are transfer-learned using two types of datasets constructed using the proposed and conventional signatures to classify six classes of space targets and a debris–cone, rounded cone, cone with empennages, cylinder, curved plate, and square plate. Among the proposed signatures, the spectrogram had lower classification accuracy than the conventional spectrogram, but the classification accuracy increased from 88.97% to 92.11% for CVD. Furthermore, when recalculated not with six classes but simply with only two classes of precessing space targets and tumbling debris, the proposed spectrogram and CVD show the classification accuracy of over 99.82% for both AlexNet and ResNet-18. Specially, for two classes, CVD provided results with higher accuracy than the spectrogram. |
format | Online Article Text |
id | pubmed-8877989 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-88779892022-02-26 Space Target Classification Improvement by Generating Micro-Doppler Signatures Considering Incident Angle Lee, Jae-In Kim, Nammon Min, Sawon Kim, Jeongwoo Jeong, Dae-Kyo Seo, Dong-Wook Sensors (Basel) Article Classifying space targets from debris is critical for radar resource management as well as rapid response during the mid-course phase of space target flight. Due to advances in deep learning techniques, various approaches have been studied to classify space targets by using micro-Doppler signatures. Previous studies have only used micro-Doppler signatures such as spectrogram and cadence velocity diagram (CVD), but in this paper, we propose a method to generate micro-Doppler signatures taking into account the relative incident angle that a radar can obtain during the target tracking process. The AlexNet and ResNet-18 networks, which are representative convolutional neural network architectures, are transfer-learned using two types of datasets constructed using the proposed and conventional signatures to classify six classes of space targets and a debris–cone, rounded cone, cone with empennages, cylinder, curved plate, and square plate. Among the proposed signatures, the spectrogram had lower classification accuracy than the conventional spectrogram, but the classification accuracy increased from 88.97% to 92.11% for CVD. Furthermore, when recalculated not with six classes but simply with only two classes of precessing space targets and tumbling debris, the proposed spectrogram and CVD show the classification accuracy of over 99.82% for both AlexNet and ResNet-18. Specially, for two classes, CVD provided results with higher accuracy than the spectrogram. MDPI 2022-02-20 /pmc/articles/PMC8877989/ /pubmed/35214555 http://dx.doi.org/10.3390/s22041653 Text en © 2022 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 (https://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Lee, Jae-In Kim, Nammon Min, Sawon Kim, Jeongwoo Jeong, Dae-Kyo Seo, Dong-Wook Space Target Classification Improvement by Generating Micro-Doppler Signatures Considering Incident Angle |
title | Space Target Classification Improvement by Generating Micro-Doppler Signatures Considering Incident Angle |
title_full | Space Target Classification Improvement by Generating Micro-Doppler Signatures Considering Incident Angle |
title_fullStr | Space Target Classification Improvement by Generating Micro-Doppler Signatures Considering Incident Angle |
title_full_unstemmed | Space Target Classification Improvement by Generating Micro-Doppler Signatures Considering Incident Angle |
title_short | Space Target Classification Improvement by Generating Micro-Doppler Signatures Considering Incident Angle |
title_sort | space target classification improvement by generating micro-doppler signatures considering incident angle |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8877989/ https://www.ncbi.nlm.nih.gov/pubmed/35214555 http://dx.doi.org/10.3390/s22041653 |
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