Radar emitter signals classification using kernel principle component analysis and fuzzy support vector machines

Author

Ren, Ming-qiu ; Zhu, Yuan-qing ; Mao, Yan ; Han, Jun

Author_Institution

Wuhan Radar Acad., Wuhan

Volume

3

fYear

2007

fDate

2-4 Nov. 2007

Firstpage

1442

Lastpage

1446

Abstract

Abstract In this paper, a novel approach based on QTFDs and kernel principle component analysis (KPCA) is proposed to extract features of radar emitter signals. Then, these discriminative and low dimensional features achieved were fed to a Support Vector Machines (SVMs) based on FCM (fuzzy c-means) clustering for multi-class pattern recognition. Experimental results show that the proposed methodology was efficient for the different complex radar emitter signals detection and classification.

Keywords

feature extraction; fuzzy set theory; principal component analysis; radar computing; radar signal processing; signal classification; support vector machines; feature extraction; fuzzy c-means; fuzzy support vector machines; kernel principle component analysis; radar emitter signals classification; Feature extraction; Kernel; Pattern analysis; Pattern classification; Pattern recognition; Radar applications; Signal analysis; Support vector machine classification; Support vector machines; Time frequency analysis; FCM clustering; Radar emitter signal classification; kernel principle component analysis; support vector machines;

fLanguage

English

Publisher

ieee

Conference_Titel

Wavelet Analysis and Pattern Recognition, 2007. ICWAPR '07. International Conference on

Conference_Location

Beijing

Print_ISBN

978-1-4244-1065-1

Electronic_ISBN

978-1-4244-1066-8

Type

conf

DOI

10.1109/ICWAPR.2007.4421662

Filename

4421662