{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,18]],"date-time":"2025-12-18T14:17:52Z","timestamp":1766067472415,"version":"build-2065373602"},"reference-count":24,"publisher":"MDPI AG","issue":"4","license":[{"start":{"date-parts":[[2022,2,20]],"date-time":"2022-02-20T00:00:00Z","timestamp":1645315200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Hanwha System (South Korea)","award":["U-19-007"],"award-info":[{"award-number":["U-19-007"]}]},{"DOI":"10.13039\/501100003725","name":"National Research Foundation of Korea","doi-asserted-by":"publisher","award":["2021R1I1A3044405"],"award-info":[{"award-number":["2021R1I1A3044405"]}],"id":[{"id":"10.13039\/501100003725","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>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\u2013cone, 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.<\/jats:p>","DOI":"10.3390\/s22041653","type":"journal-article","created":{"date-parts":[[2022,2,21]],"date-time":"2022-02-21T08:34:47Z","timestamp":1645432487000},"page":"1653","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":10,"title":["Space Target Classification Improvement by Generating Micro-Doppler Signatures Considering Incident Angle"],"prefix":"10.3390","volume":"22","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-0218-4718","authenticated-orcid":false,"given":"Jae-In","family":"Lee","sequence":"first","affiliation":[{"name":"Interdisciplinary Major of Maritime AI Convergence, Korea Maritime & Ocean University, Busan 49112, Korea"}]},{"given":"Nammon","family":"Kim","sequence":"additional","affiliation":[{"name":"Department of Land Radar, Hanwha Systems, Yongin 17121, Korea"}]},{"given":"Sawon","family":"Min","sequence":"additional","affiliation":[{"name":"Department of Land Radar, Hanwha Systems, Yongin 17121, Korea"}]},{"given":"Jeongwoo","family":"Kim","sequence":"additional","affiliation":[{"name":"Department of Land Radar, Hanwha Systems, Yongin 17121, Korea"}]},{"given":"Dae-Kyo","family":"Jeong","sequence":"additional","affiliation":[{"name":"Agency for Defense Development, Daejeon 34075, Korea"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9449-7772","authenticated-orcid":false,"given":"Dong-Wook","family":"Seo","sequence":"additional","affiliation":[{"name":"Interdisciplinary Major of Maritime AI Convergence, Korea Maritime & Ocean University, Busan 49112, Korea"}]}],"member":"1968","published-online":{"date-parts":[[2022,2,20]]},"reference":[{"key":"ref_1","first-page":"543","article-title":"Target Length Estimation of Target by Scattering Center Number Estimation Methods","volume":"38","author":"Lee","year":"2020","journal-title":"J. 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