Spectral analysis of stationary random bivariate signals
Abstract
A novel approach towards the spectral analysis of stationary random bivariate signals is proposed. Using the Quaternion Fourier Transform, we introduce a quaternion-valued spectral representation of random bivariate signals seen as complex-valued sequences. This makes possible the definition of a scalar quaternion-valued spectral density for bivariate signals. This spectral density can be meaningfully interpreted in terms of frequency-dependent polarization attributes. A natural decomposition of any random bivariate signal in terms of unpolarized and polarized components is introduced. Nonparametric spectral density estimation is investigated, and we introduce the polarization periodogram of a random bivariate signal. Numerical experiments support our theoretical analysis, illustrating the relevance of the approach on synthetic data.
Keywords
Cite
@article{arxiv.1703.06417,
title = {Spectral analysis of stationary random bivariate signals},
author = {Julien Flamant and Nicolas Le Bihan and Pierre Chainais},
journal= {arXiv preprint arXiv:1703.06417},
year = {2017}
}
Comments
11 pages, 3 figures