{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,13]],"date-time":"2026-03-13T04:00:21Z","timestamp":1773374421493,"version":"3.50.1"},"reference-count":41,"publisher":"MDPI AG","issue":"3","license":[{"start":{"date-parts":[[2020,1,21]],"date-time":"2020-01-21T00:00:00Z","timestamp":1579564800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>In the conventional neural network, deep depth is required to achieve high accuracy of recognition. Additionally, the problem of saturation may be caused, wherein the recognition accuracy is down-regulated with the increase in the number of network layers. To tackle the mentioned problem, a neural network model is proposed incorporating a micro convolutional module and residual structure. Such a model exhibits few hyper-parameters, and can extended flexibly. In the meantime, to further enhance the separability of features, a novel loss function is proposed, integrating boundary constraints and center clustering. According to the experimental results with a simulated dataset of HRRP signals obtained from thirteen 3D CAD object models, the presented model is capable of achieving higher recognition accuracy and robustness than other common network structures.<\/jats:p>","DOI":"10.3390\/s20030586","type":"journal-article","created":{"date-parts":[[2020,1,21]],"date-time":"2020-01-21T11:25:59Z","timestamp":1579605959000},"page":"586","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":13,"title":["A Neural Network with Convolutional Module and Residual Structure for Radar Target Recognition Based on High-Resolution Range Profile"],"prefix":"10.3390","volume":"20","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-0769-1647","authenticated-orcid":false,"given":"Zhequan","family":"Fu","sequence":"first","affiliation":[{"name":"Coast Defense College, Naval Aviation University, Yantai 264001, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Shangsheng","family":"Li","sequence":"additional","affiliation":[{"name":"Coast Defense College, Naval Aviation University, Yantai 264001, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xiangping","family":"Li","sequence":"additional","affiliation":[{"name":"Coast Defense College, Naval Aviation University, Yantai 264001, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Bo","family":"Dan","sequence":"additional","affiliation":[{"name":"Coast Defense College, Naval Aviation University, Yantai 264001, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xukun","family":"Wang","sequence":"additional","affiliation":[{"name":"Coast Defense College, Naval Aviation University, Yantai 264001, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2020,1,21]]},"reference":[{"key":"ref_1","first-page":"32","article-title":"Method of Aerial Target Length Extraction Based on High Resolution Range Profile","volume":"07","author":"Li","year":"2018","journal-title":"Mod. Radar"},{"key":"ref_2","first-page":"1960","article-title":"Length estimation method of ship target based on wide-band radar\u2019s HRRP","volume":"40","author":"Wei","year":"2018","journal-title":"Syst. Eng. Electron."},{"key":"ref_3","first-page":"545","article-title":"Signal Separation for Target Group in Midcourse Based on Time-frequency Filtering","volume":"05","author":"He","year":"2015","journal-title":"J. Radars"},{"key":"ref_4","first-page":"50","article-title":"Fast analysis of electromagnetic scattering characteristics in spatial and frequency domains based on compressive sensing","volume":"17","author":"Chen","year":"2014","journal-title":"Acta Phys. Sin."},{"key":"ref_5","unstructured":"Liu, S. (2016). Research on Feature Extraction and Recognition Performance Enhancement Algorithms Based on High Range Resolution Profile. [Ph.D. Dissertation, National University of Defense Technology]."},{"key":"ref_6","first-page":"2461","article-title":"Target Recognition for Polarimetric HRRP Based on Fast Density Search Clustering Method","volume":"10","author":"Wu","year":"2016","journal-title":"J. Electron. Inf. Technol."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"1935","DOI":"10.1109\/LGRS.2016.2618840","article-title":"Polarimetric SAR Image Classification Using Deep Convolutional Neural Networks","volume":"13","author":"Zhou","year":"2016","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"6425","DOI":"10.1109\/TGRS.2018.2838593","article-title":"Multiview synthetic aperture radar automatic target recognition optimization: Modeling and implementation","volume":"56","author":"Pei","year":"2018","journal-title":"IEEE Trans. Geosci. Remote. Sens."},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Fu, H., Li, Y., Wang, Y., and Li, P. (2018, January 25\u201327). Maritime Ship Targets Recognition with Deep Learning. Proceedings of the 37th Chinese Control Conference (CCC), Wuhan, China.","DOI":"10.23919\/ChiCC.2018.8484085"},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Xing, S., and Zhang, S. (2018, January 5\u20138). Ship model recognition based on convolutional neural networks. Proceedings of the 2018 IEEE International Conference on Mechatronics and Automation (ICMA), Changchun, China.","DOI":"10.1109\/ICMA.2018.8484362"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"3976","DOI":"10.1109\/TIE.2017.2764849","article-title":"A Vision-Aided Approach to Perching a Bioinspired Unmanned Aerial Vehicle","volume":"65","author":"Luo","year":"2018","journal-title":"IEEE Trans. Ind. Electron."},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Li, C., and Hao, L. (July, January 26). High-Resolution, Downward-Looking Radar Imaging Using a Small Consumer Drone. Proceedings of the 2016 IEEE Antennas and Propagation Society International Symposium (APSURSI), Fajardo, Puerto Rico.","DOI":"10.1109\/APS.2016.7696725"},{"key":"ref_13","first-page":"1121","article-title":"Radar HRRP target recognition with one-dimensional CNN","volume":"58","author":"Yin","year":"2018","journal-title":"Telecommun. Eng."},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Karabayir, O., Yucedag, O.M., Kartal, M.Z., and Serim, H.A. (2017, January 28\u201330). Convolutional neural networks-based ship target recognition using high resolution range profiles. Proceedings of the 18th International Radar Symposium (IRS), Prague, Czech Republic.","DOI":"10.23919\/IRS.2017.8008207"},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Lunden, J., and Koivunen, V. (2016, January 2\u20136). Deep learning for HRRP-based target recognition in multistatic radar systems. Proceedings of the 2016 IEEE Radar Conference, Philadelphia, PA, USA.","DOI":"10.1109\/RADAR.2016.7485271"},{"key":"ref_16","first-page":"575","article-title":"Polarimetric radar target recognition based on depth convolution neural network","volume":"33","author":"Gai","year":"2018","journal-title":"Chin. J. Radio Sci."},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Visentin, T., Sagainov, A., Hasch, J., and Zwick, T. (2017, January 13\u201316). Classification of objects in polarimetric radar images using CNNs at 77 GHz. Proceedings of the 2017 IEEE Asia Pacific Microwave Conference (APMC), Kuala Lumpur, Malaysia.","DOI":"10.1109\/APMC.2017.8251453"},{"key":"ref_18","first-page":"24","article-title":"High Resolution Range Profile Target Recognition Based on Convolutional Neural Network","volume":"39","author":"Yang","year":"2017","journal-title":"Mod. Radar"},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Yu, S., and Xie, Y. (2018, January 15\u201318). Application of a convolutional autoencoder to half space radar hrrp recognition. Proceedings of the 2018 International Conference on Wavelet Analysis and Pattern Recognition (ICWAPR), Chengdu, China.","DOI":"10.1109\/ICWAPR.2018.8521306"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"379","DOI":"10.1016\/j.patcog.2016.08.012","article-title":"Radar HRRP target recognition with deep networks","volume":"61","author":"Feng","year":"2017","journal-title":"Pattern Recogn."},{"key":"ref_21","first-page":"2433","article-title":"Radar HRRP target recognition based on convolutional sparse coding and multi-classifier fusion","volume":"11","author":"Wang","year":"2018","journal-title":"Syst. Eng. Electron"},{"key":"ref_22","unstructured":"Zhang, H. (2016). RF Stealth Based Airborne Radar System Simulation and HRRP Target Recognition Research. [Master\u2019s Dissertation, Nanjing University of Aeronautics and Astronautics]."},{"key":"ref_23","first-page":"31","article-title":"Dimension reduction method of high resolution range profile based on Autoencoder","volume":"17","author":"Zhang","year":"2016","journal-title":"J. PLA Univ. Sci. Technol. (Nat. Sci. Ed.)"},{"key":"ref_24","first-page":"149","article-title":"Radar Target Recognition Based on Stacked Denoising Sparse Autoencoder","volume":"6","author":"Zhao","year":"2017","journal-title":"J. Radars"},{"key":"ref_25","unstructured":"Krizhevsky, A., Sutskever, I., and Hinton, G.E. (2012, January 3\u20136). ImageNet classification with deep convolutional neural networks. Proceedings of the 26th Annual Conference on Neural Information Processing Systems (NIPS), Lake Tahoe, NV, USA."},{"key":"ref_26","unstructured":"Simonyan, K., and Zisserman, A. (2015, January 7\u20139). Very deep convolutional networks for large-scale image recognition. Proceedings of the 3rd International Conference on Learning Representations (ICLR), San Diego, CA, USA."},{"key":"ref_27","unstructured":"He, K., Zhang, X., Ren, S., and Sun, J. (July, January 26). Deep residual learning for image recognition. Proceedings of the 29th IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, NV, USA."},{"key":"ref_28","unstructured":"Glorot, X., and Bengio, Y. (2010, January 13\u201315). Understanding the difficulty of training deep feedforward neural networks. Proceedings of the 13th International Conference on Artificial Intelligence and Statistics (AISTATS), Sardinia, Italy."},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., and Sun, J. (2015, January 7\u201313). Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification. Proceedings of the 2015 IEEE International Conference on Computer Vision (ICCV), Santiago, Chile.","DOI":"10.1109\/ICCV.2015.123"},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Szegedy, C., Ioffe, S., Vanhoucke, V., and Alemi, A.A. (2017, January 4\u201310). Inception-v4, Inception-ResNet and the Impact of Residual Connections on Learning. Proceedings of the 31st AAAI Conference on Artificial Intelligence (AAAI), San Francisco, CA, USA.","DOI":"10.1609\/aaai.v31i1.11231"},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Liu, W., Wen, Y., Yu, Z., Li, M., Raj, B., and Song, L. (2017, January 21\u201326). SphereFace: Deep hypersphere embedding for face recognition. Proceedings of the 30th IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.713"},{"key":"ref_32","unstructured":"Liu, W., Wen, Y., Yu, Z., and Yang, M. (2016, January 19\u201324). Large-Margin Softmax Loss for Convolutional Neural Networks. Proceedings of the 33th International Conference on Machine Learning (ICML), New York, NY, USA."},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Wang, F., Xiang, X., Cheng, J., and Yuille, A.L. (2017, January 23\u201327). NormFace: L2 Hypersphere Embedding for Face Verification. Proceedings of the 25th ACM International Conference on Multimedia, Mountain View, CA, USA.","DOI":"10.1145\/3123266.3123359"},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Liu, Y., and Liu, Q. (2017, January 26\u201328). Convolutional neural networks with large-margin softmax loss function for cognitive load recognition. Proceedings of the 36th Chinese Control Conference (CCC), Dalian, China.","DOI":"10.23919\/ChiCC.2017.8027991"},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"926","DOI":"10.1109\/LSP.2018.2822810","article-title":"Additive Margin Softmax for Face Verification","volume":"25","author":"Wang","year":"2018","journal-title":"IEEE Signal Process. Lett."},{"key":"ref_36","first-page":"681","article-title":"Modified KNN rule with its application in radar HRRP target recognition","volume":"34","author":"Chen","year":"2007","journal-title":"J. Xidian Univ."},{"key":"ref_37","unstructured":"Bao, Z. (2018). Study of Radar Target Recognition Based on Continual Learning. [Master Dissertation, Xidian University]."},{"key":"ref_38","first-page":"4556","article-title":"RBF-SVM feature selection arithmetic based on kernel space mean inter-class distance","volume":"29","author":"Huang","year":"2012","journal-title":"Appl. Res. Comput."},{"key":"ref_39","first-page":"105","article-title":"A New Radar HRRP Target Recognition Method Based on Random Forest","volume":"35","author":"Yao","year":"2014","journal-title":"J. Zhengzhou Univ. (Eng. Sci.)"},{"key":"ref_40","first-page":"62","article-title":"Research on Anti-jamming Recognition Method of Aerial Infrared Target Based on Na\u00efve Bayes Classifier","volume":"2","author":"Yang","year":"2019","journal-title":"Flight Control Detect."},{"key":"ref_41","unstructured":"Liu, X. (2018). The application of a multi-layers pre-training convolutional neural network in image recognition. [Master Dissertation, South\u2013Central University for Nationalities]."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/20\/3\/586\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,13]],"date-time":"2025-10-13T13:29:59Z","timestamp":1760362199000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/20\/3\/586"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,1,21]]},"references-count":41,"journal-issue":{"issue":"3","published-online":{"date-parts":[[2020,2]]}},"alternative-id":["s20030586"],"URL":"https:\/\/doi.org\/10.3390\/s20030586","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020,1,21]]}}}