Simultaneous DFT and IDFT through Widely Linear CLMS
Xing Zhang, Bruno Scalzo Dees, Chunguo Li, Yili Xia, Luxi Yang, Danilo P. Mandic
Abstract
Complex least mean square (CLMS) based adaptive computation of discrete orthogonal transforms has been extensively investigated in the literature. However, all of these results provide only a means for the calculation of either forward orthogonal transforms or their inverse orthogonal transforms, separately. In this work, a way to simultaneously calculate the discrete Fourier transform (DFT) and the inverse DFT (IDFT) is established via the widely linear (WL) signal processing framework. We show that by appropriately selecting the input vector and adaptation speed of the widely linear complex least mean square (WL-CLMS), the resulting spectrum analyzer is capable of simultaneously performing DFT and IDFT of the signal to be Fourier analyzed in both the block-based and online manners.
BibTeX
@inproceedings{icassp2019_simultaneousdfta,
title = {Simultaneous DFT and IDFT through Widely Linear CLMS},
author = {Xing Zhang and Bruno Scalzo Dees and Chunguo Li and Yili Xia and Luxi Yang and Danilo P. Mandic},
booktitle = {ICASSP 2019},
year = {2019}
}