A novel augmented complex valued kernel LMS

Author

Tobar, Felipe A. ; Kuh, Anthony ; Mandic, Danilo P.

fYear

2012

fDate

17-20 June 2012

Firstpage

473

Lastpage

476

Abstract

A novel class of complex valued kernel least mean square (CKLMS) algorithms is introduced with the aim to provide physical meaning to the mapping between the primal and dual space termed the independent CKLMS (iCKLMS). The general class of CKLMS algorithms is also extended in the widely linear sense to develop online kernel algorithms suitable for the processing of general complex valued signals, both circular and noncircular. The so-introduced augmented complex kernel least mean square (ACKLMS) algorithms are verified on adaptive prediction of nonlinear and nonstationary complex wind signals.

Keywords

geophysical signal processing; least mean squares methods; ACKLMS; adaptive prediction; augmented complex valued kernel LMS; complex valued kernel least mean square algorithm; general complex valued signals; iCKLMS; independent CKLMS; nonlinear complex wind signals; nonstationary complex wind signals; online kernel algorithms; signal processing; Heuristic algorithms; Kernel; Least squares approximation; Prediction algorithms; Signal processing algorithms; Wind forecasting; Wind speed; Complex augmented kernels; augmented complex LMS; kernel prediction; least mean square; noncircularity; widely linear modelling; wind prediction;

fLanguage

English

Publisher

ieee

Conference_Titel

Sensor Array and Multichannel Signal Processing Workshop (SAM), 2012 IEEE 7th

Conference_Location

Hoboken, NJ

ISSN

1551-2282

Print_ISBN

978-1-4673-1070-3

Type

conf

DOI

10.1109/SAM.2012.6250542

Filename

6250542