Title :
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
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;
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
DOI :
10.1109/ICWAPR.2007.4421662
