Dynamic Independent Component/Vector Analysis: Time-Variant Linear Mixtures Separable by Time-Invariant Beamformers

0542013 - ÚTIA 2022 RIV US eng J - Journal Article
Koldovský, Z. - Kautský, V. - Tichavský, Petr - Čmejla, J. - Málek, J.
Dynamic Independent Component/Vector Analysis: Time-Variant Linear Mixtures Separable by Time-Invariant Beamformers.
IEEE Transactions on Signal Processing. Roč. 69, č. 1 (2021), s. 2158-2173. ISSN 1053-587X. E-ISSN 1941-0476
Grant - others:GA ČR(CZ) GA20-17720S
Institutional support: RVO:67985556
Keywords : Blind Source Separation * Blind Source Extraction * Independent Vector Analysis
OECD category: Electrical and electronic engineering
Impact factor: 4.875, year: 2021
Method of publishing: Limited access
http://library.utia.cas.cz/separaty/2021/SI/tichavsky-0542013.pdf https://ieeexplore.ieee.org/document/9387552

A novel extension of Independent Component and Independent Vector Analysis for blind extraction/separation of one or several sources from time-varying mixtures is proposed. The mixtures are assumed to be separable source-by-source in series or in parallel based on a recently proposed mixing model that allows for the movements of the desired source while the separating beamformer is time-invariant. The popular FastICA algorithm is extended for these mixtures in one-unit, symmetric and block-deflation variants. The algorithms are derived within a unified framework so that they are applicable in the real-valued as well as complex-valued domains, and jointly to several mixtures, similar to Independent Vector Analysis. Performance analysis of the one-unit algorithm is provided, it shows its asymptotic efficiency under the given mixing and statistical models. Numerical simulations corroborate the validity of the analysis, confirm the usefulness of the algorithms in separation of moving sources, and show the superior speed of convergence and ability to separate super-Gaussian as well as sub-Gaussian signals.
Permanent Link: http://hdl.handle.net/11104/0320294