Performance Bound for Blind Extraction of Non-Gaussian Complex-Valued Vector Component from Gaussian Background

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F46747885%3A24220%2F19%3A00006962" target="_blank" >RIV/46747885:24220/19:00006962 - isvavai.cz</a>

Nalezeny alternativní kódy

RIV/68407700:21340/19:00344850

Výsledek na webu

<a href="https://asap.ite.tul.cz/wp-content/uploads/sites/3/2019/05/icassp2019_CRLB.pdf" target="_blank" >https://asap.ite.tul.cz/wp-content/uploads/sites/3/2019/05/icassp2019_CRLB.pdf</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1109/ICASSP.2019.8683885" target="_blank" >10.1109/ICASSP.2019.8683885</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Performance Bound for Blind Extraction of Non-Gaussian Complex-Valued Vector Component from Gaussian Background

Popis výsledku v původním jazyce

Independent Vector Extraction aims at the joint blind source extraction of K dependent signals of interest (SOI) from K mixtures (one signal from one mixture). Similarly to Independent Component/Vector Analysis (ICA/IVA), the SOIs are assumed to be independent of the other signals in the mixture. Compared to IVA, the (de-)mixing IVE model is reduced in the number of parameters for the extraction problem. The SOIs are assumed to be non-Gaussian or noncircular Gaussian, while the other signals are modeled as circular Gaussian. In this paper, a Cramer-Rao-Induced Bound (CRIB) for the achievable Interference-to-Signal Ratio (ISR) is derived for IVE. The bound is compared with similar bounds for ICA, IVA, and Independent Component Extraction (ICE). Numerical simulations show a good correspondence between the empirical results and the theory.

Název v anglickém jazyce

Performance Bound for Blind Extraction of Non-Gaussian Complex-Valued Vector Component from Gaussian Background

Popis výsledku anglicky

Independent Vector Extraction aims at the joint blind source extraction of K dependent signals of interest (SOI) from K mixtures (one signal from one mixture). Similarly to Independent Component/Vector Analysis (ICA/IVA), the SOIs are assumed to be independent of the other signals in the mixture. Compared to IVA, the (de-)mixing IVE model is reduced in the number of parameters for the extraction problem. The SOIs are assumed to be non-Gaussian or noncircular Gaussian, while the other signals are modeled as circular Gaussian. In this paper, a Cramer-Rao-Induced Bound (CRIB) for the achievable Interference-to-Signal Ratio (ISR) is derived for IVE. The bound is compared with similar bounds for ICA, IVA, and Independent Component Extraction (ICE). Numerical simulations show a good correspondence between the empirical results and the theory.

Druh

D - Stať ve sborníku

CEP obor

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OECD FORD obor

10102 - Applied mathematics

Projekt

<a href="/cs/project/GA17-00902S" target="_blank" >GA17-00902S: Pokročilé metody slepé separace podprostorů</a><br>

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Rok uplatnění

2019

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název statě ve sborníku

ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings

ISBN

978-1-4799-8131-1

ISSN

1520-6149

e-ISSN

—

Počet stran výsledku

5

Strana od-do

5287-5291

Název nakladatele

IEEE

Místo vydání

Brighton, England

Místo konání akce

Brighton

Datum konání akce

1. 1. 2019

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

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