Radar emitter classification for large data set based on weighted‐xgboost. Issue 8 (9th June 2017)

• Record Type: Journal Article
• Authors: Chen, Wenbin; Fu, Kun; Zuo, Jiawei; Zheng, Xinwei; Huang, Tinglei; Ren, Wenjuan
• Is Part Of: IET radar, sonar & navigation. Volume 11:Issue 8(2017)
• Page Start: 1203
• Page End: 1207
• Publication Date: 2017-06-09
• Subjects: radar signal processing -- signal classification -- learning (artificial intelligence) -- radar computing; radar emitter classification -- large‐data set -- weighted‐xgboost model -- REC -- intercepted radar signals -- classification method -- w‐xgboost model -- complex radar signals -- continuous data -- categorical data -- discrete data -- smooth weight function -- data deviation problem -- machine learning algorithm; Signal processing -- Periodicals; Radar -- Periodicals; Sonar -- Periodicals; Electronics in navigation -- Periodicals; Navigation -- Periodicals; 621.3848
• DOI: 10.1049/iet-rsn.2016.0632
• Languages: English
• ISSNs: 1751-8784

Abstract

Abstract : 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.