Radar emitter signals classification using kernel principle component analysis and fuzzy support vector machines
Ming-Qiu Ren, Yuanqing Zhu, Yan Mao, Jun Han · 2007
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.