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access icon free Radar emitter classification for large data set based on weighted-xgboost

  • Author(s): Wenbin Chen 1, 2, 3 ; Kun Fu 1, 2 ; Jiawei Zuo 1, 2, 3 ; Xinwei Zheng 1, 2 ; Tinglei Huang 1, 2 ; Wenjuan Ren 1, 2
    • View affiliations
    • Affiliations: 1: Key Laboratory of Technology in Geo-Spatial Information Processing and Application System, Chinese Academy of Sciences, Beijing 100190, People's Republic of China ;
      2: Institute of Electronics, Chinese Academy of Sciences, Beijing 100190, People's Republic of China ;
      3: University of Chinese Academy of Sciences, Beijing100049, People's Republic of China
  • Source: Volume 11, Issue 8, August 2017, p. 1203 – 1207
    DOI:  10.1049/iet-rsn.2016.0632 , Print ISSN 1751-8784, Online ISSN 1751-8792
© The Institution of Engineering and Technology
Received 19/12/2016, Accepted 17/03/2017, Published 21/03/2017

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.

Inspec keywords: radar computing; signal classification; radar signal processing; learning (artificial intelligence)

Other keywords: data deviation problem; discrete data; REC; machine learning algorithm; weighted-xgboost model; smooth weight function; w-xgboost model; large-data set; categorical data; continuous data; classification method; intercepted radar signals; complex radar signals; radar emitter classification

Subjects: Electrical engineering computing; Knowledge engineering techniques; Signal processing and detection; Radar equipment, systems and applications

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