Cramér-Rao Bounds for Complex-Valued Independent Component Extraction: Determined and Piecewise Determined Mixing Models
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F46747885%3A24220%2F20%3A00007819" target="_blank" >RIV/46747885:24220/20:00007819 - isvavai.cz</a>
Alternative codes found
RIV/67985556:_____/20:00532740 RIV/68407700:21340/20:00344851
Result on the web
<a href="https://asap.ite.tul.cz/wp-content/uploads/sites/3/2020/09/LARGE_The_Lower_Bound_for_Separation_Accuracy_of_Independent_Vector_Extraction.pdf" target="_blank" >https://asap.ite.tul.cz/wp-content/uploads/sites/3/2020/09/LARGE_The_Lower_Bound_for_Separation_Accuracy_of_Independent_Vector_Extraction.pdf</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1109/TSP.2020.3022827" target="_blank" >10.1109/TSP.2020.3022827</a>
Alternative languages
Result language
angličtina
Original language name
Cramér-Rao Bounds for Complex-Valued Independent Component Extraction: Determined and Piecewise Determined Mixing Models
Original language description
Blind source extraction (BSE) aims at recovering an unknown source signal of interest from the observation of instantaneous linear mixtures of the sources. This paper presents Cramer-Rao lower bounds (CRLB) for the complex-valued BSE problem based on the assumption that the target signal is independent of the other signals. The target source is assumed to be non-Gaussian or non-circular Gaussian while the other signals (background) are circular Gaussian or non-Gaussian. The results confirm some previous observations known for the real domain and yield new results for the complex domain. Also, the CRLB for independent component extraction (ICE) is shown to coincide with that for independent component analysis (ICA) when the non-Gaussianity of background is taken into account. Second,we extend the CRLB analysis to piecewise determined mixing models, where the observed signals are assumed to obey thedetermined mixing model within short blocks where the mixing matrices can be varying from block to block. This model has applications, for instance, when separating dynamic mixtures. Either the mixing vector or the separating vector corresponding to the target source is assumed to be constant across the blocks.The CRLBs for the parameters of these models bring new performance limits for the BSE problem.
Czech name
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Czech description
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Classification
Type
J<sub>imp</sub> - Article in a specialist periodical, which is included in the Web of Science database
CEP classification
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OECD FORD branch
10102 - Applied mathematics
Result continuities
Project
<a href="/en/project/GA20-17720S" target="_blank" >GA20-17720S: Advanced Mixing Models for Blind Source Extraction</a><br>
Continuities
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)
Others
Publication year
2020
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Name of the periodical
IEEE Transactions on Signal Processing
ISSN
1053-587X
e-ISSN
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Volume of the periodical
68
Issue of the periodical within the volume
2020
Country of publishing house
US - UNITED STATES
Number of pages
14
Pages from-to
5230-5243
UT code for WoS article
000576252300002
EID of the result in the Scopus database
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