An improved SVDU-IKPCA algorithm for Specific Emitter Identification

Author

Dan Xu ; Bo Yang ; Wenli Jiang ; Yiyu Zhou

Author_Institution

Sch. of Electron. Sci. & Eng., Nat. Univ. of Defense Technol., Changsha

fYear

2008

fDate

20-23 June 2008

Firstpage

692

Lastpage

696

Abstract

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.

Keywords

extrapolation; forecasting theory; matrix algebra; principal component analysis; radar theory; SVDU-incremental kernel principal component analysis algorithm; extrapolation methods; forecast learning method; kernel matrix; kernel vectors; specific emitter identification; symmetrical decomposition; Amplitude modulation; Automation; Data engineering; Data mining; Frequency; Kernel; Pulse amplifiers; Pulse modulation; Radar; Technology forecasting; Dynamic Pattern Recognition; Emitter Identification; KPCA; SVDU-KPCA; Specific Emitter Identification;

fLanguage

English

Publisher

ieee

Conference_Titel

Information and Automation, 2008. ICIA 2008. International Conference on

Conference_Location

Changsha

Print_ISBN

978-1-4244-2183-1

Electronic_ISBN

978-1-4244-2184-8

Type

conf

DOI

10.1109/ICINFA.2008.4608087

Filename

4608087