{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,2]],"date-time":"2026-04-02T15:46:37Z","timestamp":1775144797553,"version":"3.50.1"},"reference-count":31,"publisher":"MDPI AG","issue":"13","license":[{"start":{"date-parts":[[2021,6,25]],"date-time":"2021-06-25T00:00:00Z","timestamp":1624579200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Hanwha Systems","award":["U-19-007"],"award-info":[{"award-number":["U-19-007"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Radar target classification is an important task in the missile defense system. State-of-the-art studies using micro-doppler frequency have been conducted to classify the space object targets. However, existing studies rely highly on feature extraction methods. Therefore, the generalization performance of the classifier is limited and there is room for improvement. Recently, to improve the classification performance, the popular approaches are to build a convolutional neural network (CNN) architecture with the help of transfer learning and use the generative adversarial network (GAN) to increase the training datasets. However, these methods still have drawbacks. First, they use only one feature to train the network. Therefore, the existing methods cannot guarantee that the classifier learns more robust target characteristics. Second, it is difficult to obtain large amounts of data that accurately mimic real-world target features by performing data augmentation via GAN instead of simulation. To mitigate the above problem, we propose a transfer learning-based parallel network with the spectrogram and the cadence velocity diagram (CVD) as the inputs. In addition, we obtain an EM simulation-based dataset. The radar-received signal is simulated according to a variety of dynamics using the concept of shooting and bouncing rays with relative aspect angles rather than the scattering center reconstruction method. Our proposed model is evaluated on our generated dataset. The proposed method achieved about 0.01 to 0.39% higher accuracy than the pre-trained networks with a single input feature.<\/jats:p>","DOI":"10.3390\/s21134365","type":"journal-article","created":{"date-parts":[[2021,6,25]],"date-time":"2021-06-25T11:07:40Z","timestamp":1624619260000},"page":"4365","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":22,"title":["Classification of Space Objects by Using Deep Learning with Micro-Doppler Signature Images"],"prefix":"10.3390","volume":"21","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-9327-5692","authenticated-orcid":false,"given":"Kwangyong","family":"Jung","sequence":"first","affiliation":[{"name":"School of Electrical and Electronic Engineering, Yonsei University, Seoul 03722, Korea"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0218-4718","authenticated-orcid":false,"given":"Jae-In","family":"Lee","sequence":"additional","affiliation":[{"name":"Interdisciplinary Major of Maritime AI Convergence, Korea Maritime & Ocean University (KMOU), Busan 49112, Korea"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0581-122X","authenticated-orcid":false,"given":"Nammoon","family":"Kim","sequence":"additional","affiliation":[{"name":"Department of Land Radar, Hanwha Systems, Yongin 17121, Korea"}]},{"given":"Sunjin","family":"Oh","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 (KMOU), Busan 49112, Korea"}]}],"member":"1968","published-online":{"date-parts":[[2021,6,25]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"1088","DOI":"10.1109\/TAES.2017.2665258","article-title":"On Model, Algorithms, and Experiment for Micro-Doppler-Based Recognition of Ballistic Targets","volume":"53","author":"Persico","year":"2017","journal-title":"IEEE Trans. Aerosp. Electron. Syst."},{"key":"ref_2","first-page":"379","article-title":"Laser Radar in Ballistic Missile Defense","volume":"22","author":"Bankman","year":"2001","journal-title":"Johns Hopkins APL Tech. Dig."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"3168","DOI":"10.1109\/TAES.2019.2905281","article-title":"Novel Classification Algorithm for Ballistic Target Based on HRRP Fram","volume":"55","author":"Persico","year":"2019","journal-title":"IEEE Trans. Aerosp. Electron. Syst."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"1969","DOI":"10.1109\/TAES.2010.5595607","article-title":"Micro-Doppler Signature Extraction from Ballistic Target with Micro-Motions","volume":"46","author":"Gao","year":"2010","journal-title":"IEEE Trans. Aerosp. Electron. Syst."},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Chen, V.C., Tahmoush, D., and Miceli, W.J. (2014). Radar Micro-Doppler Signature: Processing and Applications, The Institution of Engineering and Technology.","DOI":"10.1049\/PBRA034E"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"417","DOI":"10.1109\/TAES.2014.130762","article-title":"A novel algorithm for radar classification based on doppler characteristics exploiting orthogonal Pseudo-Zernike polynomials","volume":"51","author":"Clemente","year":"2015","journal-title":"IEEE Trans. Aerosp. Electron. Syst."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"1388","DOI":"10.1049\/el.2014.1913","article-title":"Coning target micro-motion feature extraction via scattering centre reconstruction","volume":"50","author":"He","year":"2014","journal-title":"Electron. Lett."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"705","DOI":"10.14429\/dsj.58.1697","article-title":"Ballistic Missile Warhead Recognition based on Micro-Doppler Frequency","volume":"58","author":"Zheng","year":"2008","journal-title":"Def. Sci. J."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"129","DOI":"10.1049\/el.2012.3819","article-title":"Micro-motion modelling and analysis of extended ballistic targets based on inertial parameters","volume":"49","author":"He","year":"2013","journal-title":"Electron. Lett."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"770","DOI":"10.1049\/el.2011.0580","article-title":"Nutation feature extraction of ballistic missile warhead","volume":"47","author":"Huixia","year":"2011","journal-title":"Electron. Lett."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"617","DOI":"10.1049\/el.2013.0302","article-title":"Precession feature extraction of warhead with empennages","volume":"49","author":"Yao","year":"2013","journal-title":"Electron. Lett."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"1000","DOI":"10.1109\/36.752218","article-title":"Shape estimation of space debris using single-range Doppler interferometry","volume":"37","author":"Sato","year":"1999","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"1628","DOI":"10.1007\/s11432-010-4027-4","article-title":"Time-frequency characteristics based motion estimation and imaging for high speed spinning targets via narrowband waveforms","volume":"53","author":"Zhang","year":"2010","journal-title":"Sci. China Inf. Sci."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"2892","DOI":"10.1109\/JSEN.2018.2800053","article-title":"Micro-Doppler Curves Extraction and Parameters Estimation for Cone-Shaped Target With Occlusion Effect","volume":"18","author":"Zhou","year":"2018","journal-title":"IEEE Sensors J."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"1452","DOI":"10.1049\/el.2018.6584","article-title":"Efficient 3DFV for improved discrimination of ballistic war-head","volume":"54","author":"Choi","year":"2019","journal-title":"Electron. Lett."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"1268","DOI":"10.1049\/iet-rsn.2018.5237","article-title":"Convolutional neural network for classifying space target of the same shape by using RCS time series","volume":"12","author":"Chen","year":"2018","journal-title":"IET Radar Sonar Navig."},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Garg, T., Garg, M., Mahela, O.P., and Garg, A.R. (2020). Convolutional Neural Networks with Transfer Learning for Recognition of COVID-19: A Comparative Study of Different Approaches. AI, 1.","DOI":"10.3390\/ai1040034"},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Duong, B.P., Kim, J.Y., Jeong, I., Im, K., Kim, C.H., and Kim, J.M. (2020). A Deep-Learning-Based Bearing Fault Diagnosis Using Defect Signature Wavelet Image Visualization. Appl. Sci., 10.","DOI":"10.3390\/app10248800"},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Neupane, D., and Seok, J. (2020). A Review on Deep Learning-Based Approaches for Automatic Sonar Target Recognition. Electronics, 9.","DOI":"10.3390\/electronics9111972"},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Li, X., He, Y., and Jing, X. (2019). A Survey of Deep Learning-Based Human Activity Recognition in Radar. Remote Sens., 11.","DOI":"10.3390\/rs11091068"},{"key":"ref_21","unstructured":"Alnujaim, I., Oh, D., Park, I., and Kim, Y. (April, January 31). Classification of Micro-Doppler Signatures Measured by Doppler Radar Through Transfer Learning. Proceedings of the 2019 13th European Conference on Antennas and Propagation (EuCAP), Krakow, Poland."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"396","DOI":"10.1109\/LGRS.2019.2919770","article-title":"Generative Adversarial Networks for Classification of Micro-Doppler Signatures of Human Activity","volume":"17","author":"Alnujaim","year":"2019","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Wang, Y., Feng, C., Zhang, Y., and Ge, Q. (2019, January 20\u201322). Classification of Space Targets with Micro-motion Based on Deep CNN. Proceedings of the 2019 IEEE 2nd International Conference on Electronic Information and Communication Technology (ICEICT), Harbin, China.","DOI":"10.1109\/ICEICT.2019.8846441"},{"key":"ref_24","first-page":"1097","article-title":"Imagenet classification with deep convolutional neural net-works","volume":"25","author":"Krizhevsky","year":"2012","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., and Sun, J. (2016, January 27\u201330). Deep residual learning for image recognition. Proceedings of the 2016 IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.90"},{"key":"ref_26","unstructured":"Simonyan, K., and Zisserman, A. (2014). Very deep convolutional networks for large-scale image recognition. arXiv."},{"key":"ref_27","unstructured":"Iandola, F.N., Han, S., Moskewicz, M.W., Ashraf, K., Dally, W.J., and Keutzer, K. (2016). SqueezeNet: AlexNet-level accuracy with 50x fewer parameters and< 0.5 MB model size. arXiv."},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S., Anguelov, D., Erhan, D., Vanhoucke, V., and Rabinovich, A. (2015, January 7\u201312). Going Deeper with Convolutions. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Boston, MA, USA.","DOI":"10.1109\/CVPR.2015.7298594"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"1020","DOI":"10.1109\/LAWP.2018.2829826","article-title":"Fast ISAR Image Formations Over Multiaspect Angles Using the Shooting and Bouncing Rays","volume":"17","author":"Lee","year":"2018","journal-title":"IEEE Antennas Wirel. Propag. Lett."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"2","DOI":"10.1109\/TAES.2006.1603402","article-title":"Micro-doppler effect in radar: Phenomenon, model, and simulation study","volume":"42","author":"Chen","year":"2006","journal-title":"IEEE Trans. Aerosp. Electron. Syst."},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Dai, J., and Wang, J. (2016, January 10\u201313). Recognition of warheads based on features of range profiles in ballistic missile defense. Proceedings of the 2016 CIE International Conference on Radar (RADAR), Guangzhou, China.","DOI":"10.1109\/RADAR.2016.8059177"}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/21\/13\/4365\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T06:24:24Z","timestamp":1760163864000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/21\/13\/4365"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,6,25]]},"references-count":31,"journal-issue":{"issue":"13","published-online":{"date-parts":[[2021,7]]}},"alternative-id":["s21134365"],"URL":"https:\/\/doi.org\/10.3390\/s21134365","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,6,25]]}}}