Radar source identification method based on sample reduction and improved support vector machine

DOI

10.2991/amcce-17.2017.56How to use a DOI?

Keywords

Radar emitter identification, Boundary extraction, Support vector machine

Abstract

Aiming at the problem of low efficiency of radar emitter identification method, a new method based on sample reduction and improved support vector machine is studied. Firstly, for removed redundant information, at the same time reduce the training data, the algorithm through the local normal vector to boundary extraction of sample prior information in the database. Then using the Sequential Minimal Optimization algorithm, multi classification and cross-validation to improve the original SVM. Through the improved algorithm train the reduced samples, and get the optimal model parameters. Finally using the optimal identification model to recognize the unknown pulse sequence information. Through simulation results and comparison, it is proved that the proposed radar source identification method based on sample reduction and improved support vector machine not only have high identification accuracy and robustness, but also have a good timeliness.

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  - Wengao Chen
AU  - Xin Jia
AU  - Xiaojing Tang
PY  - 2017/03
DA  - 2017/03
TI  - Radar source identification method based on sample reduction and improved support vector machine
BT  - Proceedings of the 2017 2nd International Conference on Automation, Mechanical Control and Computational Engineering (AMCCE 2017)
PB  - Atlantis Press
SP  - 321
EP  - 325
SN  - 2352-5401
UR  - https://doi.org/10.2991/amcce-17.2017.56
DO  - 10.2991/amcce-17.2017.56
ID  - Chen2017/03
ER  -