Title :
A novel augmented complex valued kernel LMS
Author :
Tobar, Felipe A. ; Kuh, Anthony ; Mandic, Danilo P.
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;
Conference_Titel :
Sensor Array and Multichannel Signal Processing Workshop (SAM), 2012 IEEE 7th
Conference_Location :
Hoboken, NJ
Print_ISBN :
978-1-4673-1070-3
DOI :
10.1109/SAM.2012.6250542
