An Improved SVDU-IKPCA Algorithm for Specific Emitter Identification

摘要：

A forecast learning method of Kernel Principal Component Analysis (KPCA)is presented for Specific Emitter Identification (SEI)application.By constructing a symmetrical decomposition of the kernel matrix,we derived a new algorithm of Incremental KPCA.Based on it,the forecast capability is developed by creating dummy samples whose kernel vectors are an extrapolation of the kernel matrix.The advance of the algorithm is verified in the SEI numerical experiment.

关键词： Emitter Identification Specific Emitter Identification KPCA SVDU-KPCA Dynamic Pattern Recognition

作者: XU Dan YANG Bo JIANG Wenli ZHOU Yiyu

作者单位: School of Electronic Science and Engineering National University of Defense Technology Changsha,410073,China

会议类型: 国际会议

会议名称: 2008 IEEE International Conference on Onformation and Automation(IEEE 信息与自动化国际会议)

会议地点: 张家界

会议语种:英文

页码: 692-696

在线出版日期: 2008-06-20（万方平台首次上网日期，不代表论文的发表时间）