Bibliography

Conference Paper (international conference)

Performance bound for blind extraction of non-Gaussian complex-valued vector component from Gaussian background

Kautský Václav, Koldovský Z., Tichavský Petr

: 2019 IEEE International Conference on Acoustics, Speech, and Signal Processing ICASSP 2019, p. 5287-5291

: 2019 IEEE International Conference on Acoustics, Speech, and Signal Processing ICASSP 2019, (Brighton, GB, 20190512)

: GA17-00902S, GA ČR

: Blind Source Extraction, Independent Component Analysis, Independent Vector Analysis

: 10.1109/ICASSP.2019.8683885

: http://library.utia.cas.cz/separaty/2019/SI/tichavsky-0505138.pdf

(eng): 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.

: BB

: 20205