Radar emitter classification for large data set based on weighted-xgboost - Chen, Wenbin; Fu, Kun; Zuo, Jiawei; Zheng, Xinwei; Huang, Tinglei; Ren, Wenjuan (IET radar, sonar & navigation, 2017)
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.