Radar emitter recognition method based on AdaBoost and decision tree

DOI

10.2991/amcce-17.2017.57How to use a DOI?

Keywords

AdaBoost, decision tree, classifier, radar emitter recognition

Abstract

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.

Copyright

© 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/).

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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  -