Radar emitter recognition method based on AdaBoost and decision tree
Xiaojing Tang, Weigao Chen, WeiGang Zhu
Available Online March 2017.
- 10.2991/amcce-17.2017.57How to use a DOI?
- AdaBoost, decision tree, classifier, radar emitter recognition
For the poor real-time, robustness and low recognition accuracy of traditional radar emitter recognition algorithm in the current high density signal environment, this paper studied a kind of radar source recognition algorithm based on decision tree and AdaBoost. Firstly, the information gain can be used to construct single decision tree. Then using AdaBoost algorithm to train the weak classifier, and get a strong classifier. Finally, get the recognition results through the strong classifier. Simulation results show that the recognition accuracy of proposed method can reach 93.78% with 10% parameter error, and the time consumption is lower than 1.5s, which has a good recognition effect.
- © 2017, the Authors. Published by Atlantis Press.
- Open Access
- This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).
Cite this article
TY - CONF AU - Xiaojing Tang AU - Weigao Chen AU - WeiGang Zhu PY - 2017/03 DA - 2017/03 TI - Radar emitter recognition method based on AdaBoost and decision tree BT - Proceedings of the 2017 2nd International Conference on Automation, Mechanical Control and Computational Engineering (AMCCE 2017) PB - Atlantis Press SP - 326 EP - 330 SN - 2352-5401 UR - https://doi.org/10.2991/amcce-17.2017.57 DO - 10.2991/amcce-17.2017.57 ID - Tang2017/03 ER -