none 10.1049/iet-rsn.2016.0632 Institution of Engineering and Technology (IET) Institution of Engineering and Technology (IET) 265 89013517 66218 2025102806123400450 10.1049 2025-10-28T16:46:50Z 2017-03-23T00:33:50Z 84 IET Radar, Sonar & Navigation IET Radar Sonar & Navi 1751-8784 1751-8792 08 2017 11 8 10.1049/rsn2.v11.8 https://ietresearch.onlinelibrary.wiley.com/toc/17518792/11/8 Wenbin Chen University of Chinese Academy of Sciences Beijing 100049 People's Republic of China Institute of Electronics Chinese Academy of Sciences Beijing 100190 People's Republic of China Key Laboratory of Technology in Geo‐Spatial Information Processing and Application System Chinese Academy of Sciences Beijing 100190 People's Republic of China Kun Fu Institute of Electronics Chinese Academy of Sciences Beijing 100190 People's Republic of China Key Laboratory of Technology in Geo‐Spatial Information Processing and Application System Chinese Academy of Sciences Beijing 100190 People's Republic of China Jiawei Zuo University of Chinese Academy of Sciences Beijing 100049 People's Republic of China Institute of Electronics Chinese Academy of Sciences Beijing 100190 People's Republic of China Key Laboratory of Technology in Geo‐Spatial Information Processing and Application System Chinese Academy of Sciences Beijing 100190 People's Republic of China Xinwei Zheng Institute of Electronics Chinese Academy of Sciences Beijing 100190 People's Republic of China Key Laboratory of Technology in Geo‐Spatial Information Processing and Application System Chinese Academy of Sciences Beijing 100190 People's Republic of China Tinglei Huang Institute of Electronics Chinese Academy of Sciences Beijing 100190 People's Republic of China Key Laboratory of Technology in Geo‐Spatial Information Processing and Application System Chinese Academy of Sciences Beijing 100190 People's Republic of China Wenjuan Ren Institute of Electronics Chinese Academy of Sciences Beijing 100190 People's Republic of China Key Laboratory of Technology in Geo‐Spatial Information Processing and Application System Chinese Academy of Sciences Beijing 100190 People's Republic of China Radar emitter classification (REC) is very important in both civil and military fields. It becomes more and more difficult to classify the intercepted radar signals with the increasing complexity of radar signals. An efficient classification method using weighted‐xgboost (w‐xgboost) model for the complex radar signals is proposed in this study. The xgboost method is widely used by data scientists and performs very well in many machine learning projects. The authors use a large data set which consists of different types of attributes (such as continuous data, categorical data, and discrete data) to train the model. A smooth weight function is introduced to solve the data deviation problem. Experiment results show that the authors’ w‐xgboost method achieves a better performance than several existing machine learning algorithms on the test set. 06 09 2017 08 2017 1203 1207 10.1049/iet-rsn.2016.0632 National Natural Science Foundation of China https://doi.org/10.13039/501100001809 41501485 http://onlinelibrary.wiley.com/termsAndConditions#vor 10.1049/iet-rsn.2016.0632 https://ietresearch.onlinelibrary.wiley.com/doi/10.1049/iet-rsn.2016.0632 https://ietresearch.onlinelibrary.wiley.com/doi/pdf/10.1049/iet-rsn.2016.0632 https://onlinelibrary.wiley.com/doi/pdf/10.1049/iet-rsn.2016.0632 https://onlinelibrary.wiley.com/doi/full-xml/10.1049/iet-rsn.2016.0632 10.3390/s130100848 10.1109/22.989948 The identification of radar signals by histogram description leads to cluster analysis processing Zak J. 1 2014 Applying the radiated emission to the emitter identification Dudczyk J. 431 2004 10.1049/el.2014.1458 10.1109/CJECE.2015.2496143 Bull. Pol. Acad. Sci., Tech. Sci. Dudczyk J. 623 61 3 2013 Identification of emitter sources in the aspect of their fractal features 10.1155/2016/1879327 10.3390/s16030289 10.1109/TAES.2014.130762 Radar emitter signal recognition based on time–frequency analysis Yang L. 1 2013 10.1049/iet-rsn.2015.0580 10.1016/j.asoc.2013.04.021 10.1016/j.procs.2013.09.206 10.1126/science.1127647 10.1038/nature14539 10.1016/j.neunet.2014.09.003 The radar target recognition research based on improved neural network algorithm Duan P. 1074 2014 Study on radar emitter recognition signal based on rough sets and RBF neural network Zhang Z. 1225 2009 10.1214/aos/1013203451 Linear, machine learning and probabilistic approaches for time series analysis Pavlyshenko B.M. 377 2016 Learning Burges C.J. 23 11 2010 From ranknet to lambdarank to lambdamart: an overview 10.1145/2939672.2939785 10.1049/iet-spr.2010.0070