{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,3]],"date-time":"2025-12-03T17:47:26Z","timestamp":1764784046951,"version":"build-2065373602"},"reference-count":49,"publisher":"MDPI AG","issue":"1","license":[{"start":{"date-parts":[[2018,1,10]],"date-time":"2018-01-10T00:00:00Z","timestamp":1515542400000},"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>A novel radar high-resolution range profile (HRRP) target recognition method based on a stacked autoencoder (SAE) and extreme learning machine (ELM) is presented in this paper. As a key component of deep structure, the SAE does not only learn features by making use of data, it also obtains feature expressions at different levels of data. However, with the deep structure, it is hard to achieve good generalization performance with a fast learning speed. ELM, as a new learning algorithm for single hidden layer feedforward neural networks (SLFNs), has attracted great interest from various fields for its fast learning speed and good generalization performance. However, ELM needs more hidden nodes than conventional tuning-based learning algorithms due to the random set of input weights and hidden biases. In addition, the existing ELM methods cannot utilize the class information of targets well. To solve this problem, a regularized ELM method based on the class information of the target is proposed. In this paper, SAE and the regularized ELM are combined to make full use of their advantages and make up for each of their shortcomings. The effectiveness of the proposed method is demonstrated by experiments with measured radar HRRP data. The experimental results show that the proposed method can achieve good performance in the two aspects of real-time and accuracy, especially when only a few training samples are available.<\/jats:p>","DOI":"10.3390\/s18010173","type":"journal-article","created":{"date-parts":[[2018,1,10]],"date-time":"2018-01-10T12:41:10Z","timestamp":1515588070000},"page":"173","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":53,"title":["Radar HRRP Target Recognition Based on Stacked Autoencoder and Extreme Learning Machine"],"prefix":"10.3390","volume":"18","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-8824-0185","authenticated-orcid":false,"given":"Feixiang","family":"Zhao","sequence":"first","affiliation":[{"name":"College of Electronic Science, National University of Defense Technology, Changsha 410073, China"}]},{"given":"Yongxiang","family":"Liu","sequence":"additional","affiliation":[{"name":"College of Electronic Science, National University of Defense Technology, Changsha 410073, China"}]},{"given":"Kai","family":"Huo","sequence":"additional","affiliation":[{"name":"College of Electronic Science, National University of Defense Technology, Changsha 410073, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7496-5433","authenticated-orcid":false,"given":"Shuanghui","family":"Zhang","sequence":"additional","affiliation":[{"name":"College of Electronic Science, National University of Defense Technology, Changsha 410073, China"}]},{"given":"Zhongshuai","family":"Zhang","sequence":"additional","affiliation":[{"name":"College of Electronic Science, National University of Defense Technology, Changsha 410073, China"}]}],"member":"1968","published-online":{"date-parts":[[2018,1,10]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"3650","DOI":"10.1109\/TGRS.2013.2274478","article-title":"Micromotion characteristic acquisition based on wideband radar phase","volume":"52","author":"Liu","year":"2014","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"300","DOI":"10.1049\/iet-rsn:20060049","article-title":"Radar target classification using multiple perspectives","volume":"1","author":"Vespe","year":"2007","journal-title":"IET Radar Sonar Navig."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"18","DOI":"10.1049\/iet-rsn:20050119","article-title":"Radar automatic target recognition using complex high-resolution range profiles","volume":"1","author":"Du","year":"2007","journal-title":"IET Radar Sonar Navig."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"3182","DOI":"10.1109\/TSP.2011.2141664","article-title":"Bayesian spatiotemporal multitask learning for radar HRRP target recognition","volume":"59","author":"Du","year":"2011","journal-title":"IEEE Trans. Signal Process."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"475","DOI":"10.1049\/el.2015.3583","article-title":"Scale-space theory-based multi-scale features for aircraft classification using HRRP","volume":"52","author":"Liu","year":"2016","journal-title":"Electron. Lett."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"1875","DOI":"10.1109\/78.942617","article-title":"A new feature vector using selected bispectra for signal classification with application in radar target recognition","volume":"49","author":"Zhang","year":"2001","journal-title":"IEEE Trans. Signal Process."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"3546","DOI":"10.1109\/TSP.2012.2191965","article-title":"Noise robust radar HRRP target recognition based on multitask factor analysis with small training data size","volume":"60","author":"Du","year":"2012","journal-title":"IEEE Trans. Signal Process."},{"key":"ref_8","first-page":"422","article-title":"Non-cooperative target recognition by means of singular value decomposition applied to radar high resolution range profiles","volume":"15","author":"Bravo","year":"2015","journal-title":"Sensors"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"186","DOI":"10.1049\/iet-rsn.2015.0145","article-title":"Non-cooperative identification of civil aircraft using a generalised mutual subspace method","volume":"10","author":"Bravo","year":"2016","journal-title":"IET Radar Sonar Navig."},{"key":"ref_10","unstructured":"Slomka, J.S. (1999, January 22\u201325). Features for high resolution radar range profile based ship classification. Proceedings of the Fifth International Symposium on Signal Processing and its Applications (ISSPA), Brisbane, Australia."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"2558","DOI":"10.1109\/TAES.2011.6034651","article-title":"Maritime ATR using classifier combination and high resolution range profiles","volume":"47","author":"Christopher","year":"2011","journal-title":"IEEE Trans. Aerosp. Electron. Syst."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"281","DOI":"10.1109\/78.485924","article-title":"Efficient mixed-spectrum estimation with application to feature extraction","volume":"42","author":"Li","year":"1996","journal-title":"IEEE Trans. Signal Process."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"1067","DOI":"10.1109\/TAES.2005.1541451","article-title":"Multi-aspect radar target recognition method based on scattering centers and HMMs classifiers","volume":"41","author":"Pei","year":"2005","journal-title":"IEEE Trans. Aerosp. Electron. Syst."},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Jiang, Y., Han, Y.B., and Sheng, W.X. (2016, January 16\u201318). Target recognition of radar HRRP using manifold learning with feature weighting. Proceedings of the 2016 IEEE International Workshop on Electromagnetics: Applications and Student Innovation Competition (iWEM), Nanjing, China.","DOI":"10.1109\/iWEM.2016.7505053"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"1602","DOI":"10.1049\/el.2017.2960","article-title":"Noise-robust HRRP target recognition method via sparse-low-rank representation","volume":"53","author":"Li","year":"2017","journal-title":"Electron. Lett."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"504","DOI":"10.1126\/science.1127647","article-title":"Reducing the dimensionality of data with neural networks","volume":"313","author":"Hinton","year":"2006","journal-title":"Science"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"436","DOI":"10.1038\/nature14539","article-title":"Deep learning","volume":"521","author":"Lecun","year":"2015","journal-title":"Nature"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"1609","DOI":"10.1109\/LGRS.2017.2726098","article-title":"Radar HRRP target recognition based on t-SNE segmentation and discriminant deep belief network","volume":"14","author":"Pan","year":"2017","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"171","DOI":"10.1016\/j.measurement.2016.04.007","article-title":"A sparse auto-encoder-based deep neural network approach for induction motor faults classification","volume":"89","author":"Sun","year":"2016","journal-title":"Measurement"},{"key":"ref_20","first-page":"149","article-title":"Radar target recognition based on stacked denoising sparse autoencoder","volume":"6","author":"Zhao","year":"2017","journal-title":"Chin. J. Radar"},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Kang, M., Ji, K., Leng, X., Xing, X., and Zou, H. (2017). Synthetic aperture radar target recognition with feature fusion based on a stacked autoencoder. Sensors, 17.","DOI":"10.3390\/s17010192"},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Protopapadakis, E., Voulodimos, A., Doulamis, A., Doulamis, N., Dres, D., and Bimpas, M. (2017). Stacked autoencoders for outlier detection in over-the-horizon radar signals. Comput. Intell. Neurosci.","DOI":"10.1155\/2017\/5891417"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"1725","DOI":"10.1049\/el.2016.3060","article-title":"Radar HRRP recognition based on discriminant deep autoencoders with small training data size","volume":"52","author":"Pan","year":"2016","journal-title":"Electron. Lett."},{"key":"ref_24","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 Recognit."},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Zhai, Y., Chen, B., Zhang, H., and Wang, Z.J. (2017, January 22\u201323). Robust variational auto-encoder for radar HRRP target recognition. Proceedings of the International Conference on Intelligent Science and Big Data Engineering, Dalian, China.","DOI":"10.1007\/978-3-319-67777-4_31"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"489","DOI":"10.1016\/j.neucom.2005.12.126","article-title":"Extreme learning machine: Theory and applications","volume":"70","author":"Huang","year":"2006","journal-title":"Neurocomputing"},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Mou, R., Chen, Q., and Huang, M. (2012, January 10\u201312). An improved BP neural network and its application. Proceedings of the 2012 Fourth International Conference on Computational and Information Sciences, Chongqing, China.","DOI":"10.1109\/ICCIS.2012.68"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"809","DOI":"10.1109\/TNNLS.2015.2424995","article-title":"Extreme learning machine for multilayer perceptron","volume":"27","author":"Tang","year":"2016","journal-title":"IEEE Trans. Neural Netw. Learn. Syst."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"32","DOI":"10.1016\/j.neunet.2014.10.001","article-title":"Trends in extreme learning machines: A review","volume":"61","author":"Huang","year":"2015","journal-title":"Neural Netw."},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Wang, H., Fan, W., Sun, F.W., and Qian, X.J. (2015, January 18\u201320). An adaptive ensemble model of extreme learning machine for time series prediction. Proceedings of the 12th International Computer Conference on Wavelet Active Media Technology and Information Processing (ICCWAMTIP), Chengdu, China.","DOI":"10.1109\/ICCWAMTIP.2015.7493911"},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"513","DOI":"10.1109\/TSMCB.2011.2168604","article-title":"Extreme learning machine for regression and multiclass classification","volume":"42","author":"Huang","year":"2012","journal-title":"IEEE Trans. Syst. Man Cybern. B Cybern."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"1411","DOI":"10.1109\/TNN.2006.880583","article-title":"A fast and accurate online sequential learning algorithm for feedforward networks","volume":"17","author":"Liang","year":"2006","journal-title":"IEEE Trans. Neural Netw."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"1352","DOI":"10.1109\/TNN.2009.2024147","article-title":"Error minimized extreme learning machine with growth of hidden nodes and incremental learning","volume":"20","author":"Feng","year":"2009","journal-title":"IEEE Trans. Neural Netw."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"306","DOI":"10.1016\/j.neucom.2005.03.002","article-title":"Fully complex extreme learning machine","volume":"68","author":"Li","year":"2005","journal-title":"Neurocomputing"},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"3460","DOI":"10.1016\/j.neucom.2007.10.008","article-title":"Enhanced random search based incremental extreme learning machine","volume":"71","author":"Huang","year":"2008","journal-title":"Neurocomputing"},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"853","DOI":"10.1080\/2150704X.2013.805279","article-title":"Kernel-based extreme learning machine for remote-sensing image classification","volume":"4","author":"Pal","year":"2013","journal-title":"Remote Sens. Lett."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"632","DOI":"10.1049\/iet-rsn.2014.0325","article-title":"Radar target classification using support vector machine and subspace methods","volume":"9","author":"Liu","year":"2015","journal-title":"IET Radar Sonar Navig."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"73","DOI":"10.1007\/s11265-013-0730-x","article-title":"Evolutionary extreme learning machine and its application to image analysis","volume":"73","author":"Liu","year":"2013","journal-title":"J. Signal Proc. Syst. Signal Image Video Technol."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"587","DOI":"10.1007\/s13042-015-0351-8","article-title":"Unsupervised extreme learning machine with representational features","volume":"8","author":"Ding","year":"2017","journal-title":"Int. J. Mach. Learn. Cybern."},{"key":"ref_40","first-page":"1647","article-title":"Traffic sign recognition using kernel extreme learning machines with deep perceptual features","volume":"18","author":"Zeng","year":"2017","journal-title":"IEEE Trans. Intell. Trans. Syst."},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Yang, X.Y., Pang, S., Shen, W., Lin, X.S., Jiang, K.Y., and Wang, Y.H. (2016). Aero engine fault diagnosis using an optimized extreme learning machine. Int. J. Aerosp. Eng.","DOI":"10.1155\/2016\/7892875"},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Zhao, F.X., Liu, Y.X., Huo, K., and Zhang, Z.S. (2017). Radar Target Classification Using an Evolutionary Extreme Learning Machine Based on Improved Quantum-Behaved Particle Swarm Optimization. Math. Probl. Eng.","DOI":"10.1155\/2017\/7273061"},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"Kumar, V., Nandi, G.C., and Kala, R. (2014, January 7\u20139). Static hand gesture recognition using stacked denoising sparse autoencoders. Proceedings of the Seventh International Conference on Contemporary Computing (IC3), Noida, India.","DOI":"10.1109\/IC3.2014.6897155"},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"79","DOI":"10.1214\/aoms\/1177729694","article-title":"On information and sufficiency","volume":"22","author":"Kullback","year":"1951","journal-title":"Ann. Math. Stat."},{"key":"ref_45","doi-asserted-by":"crossref","unstructured":"Peng, C., Yan, J., Duan, S.K., Wang, L.D., Jia, P.F., and Zhang, S.L. (2016). Enhancing electronic nose performance based on a novel QPSO-KELM model. Sensors, 16.","DOI":"10.3390\/s16040520"},{"key":"ref_46","unstructured":"Rao, C.R., and Mitra, S.K. (1971). Generalized Inverse of Matrices and Its Applications, Wiley."},{"key":"ref_47","unstructured":"Serre, D. (2002). Matrices: Theory and Applications, Springer."},{"key":"ref_48","unstructured":"Fletcher, R. (1981). Practical Methods of Optimization: Constrained Optimization, Wiley."},{"key":"ref_49","unstructured":"Yan, D.Q., Chu, Y.H., Zhang, H.Y., and Liu, D.S. (2016). Information discriminative extreme learning machine. Soft Comput., 1\u201313."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/18\/1\/173\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T14:50:47Z","timestamp":1760194247000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/18\/1\/173"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2018,1,10]]},"references-count":49,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2018,1]]}},"alternative-id":["s18010173"],"URL":"https:\/\/doi.org\/10.3390\/s18010173","relation":{},"ISSN":["1424-8220"],"issn-type":[{"type":"electronic","value":"1424-8220"}],"subject":[],"published":{"date-parts":[[2018,1,10]]}}}