A GMM-based Algorithm for Classification of Radar emitters

Author

Gong, Xuhua ; Meng, Huadong ; Wang, Xiqin

Author_Institution

Dept. of Electron. Eng., Tsinghua Univ., Beijing

fYear

2008

Firstpage

2434

Lastpage

2437

Abstract

A Gaussian mixture model (GMM)-based algorithm for the classification of radar emitters in autonomous electronic support measure systems is described in this paper. We first build a Gaussian model for every radar emitter, and then use the expectation-maximization (EM) algorithm to train the parameters of the model. Finally, we construct a classifier whose input is the parameters of pulse and whose output is the type of radar. Results of experiments and comparisons with neural network algorithm (fuzzy ARTMAP) demonstrate that the proposed algorithm is effective in the condition of low SNR.

Keywords

Gaussian processes; expectation-maximisation algorithm; neural nets; radar transmitters; signal classification; GMM-based algorithm; Gaussian mixture model; autonomous electronic support measure systems; expectation-maximization algorithm; fuzzy ARTMAP; low SNR; neural network algorithm; radar emitter classification; Classification algorithms; Libraries; Neural networks; Pulse measurements; Pulse modulation; Radar applications; Radar measurements; Radio frequency; Receivers; Space vector pulse width modulation; Classification of Radar Emitters; EM Algorithm; Gaussian Mixture Model (GMM); Neural Network (NN);

fLanguage

English

Publisher

ieee

Conference_Titel

Signal Processing, 2008. ICSP 2008. 9th International Conference on

Conference_Location

Beijing

Print_ISBN

978-1-4244-2178-7

Electronic_ISBN

978-1-4244-2179-4

Type

conf

DOI

10.1109/ICOSP.2008.4697641

Filename

4697641