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Título: Blind identification of underdetermined mixtures based on the characteristic function: the complex case
Autor(es): Almeida, André Lima Férrer de
Luciani, Xavier
Comon, Pierre
Palavras-chave: Tensor(Cálculo)
Data do documento: Fev-2011
Editor: IEEE Transactions on Signal Processing
Citação: ALMEIDA, A. L. F. de; LUCIANI, X.; COMON, P. (2011)
Abstract: Blind identification of underdetermined mixtures can be addressed efficiently by using the second Characteristic Function (CAF) of the observations. Our contribution is twofold. First, we propose the use of a Levenberg-Marquardt algorithm, herein called LEMACAF, as an alternative to an Alternating Least Squares algorithm known as ALESCAF, which has been used recently in the case of real mixtures of real sources. Second, we extend the CAF approach to the case of complex sources for which the previous algorithms are not suitable. We show that the complex case involves an appropriate tensor stowage, which is linked to a particular tensor decomposition. An extension of the LEMACAF algorithm, called LEMACAFC then proposed to blindly estimate the mixing matrix by exploiting this tensor decomposition. In our simulation results, we first provide performance comparisons between third- and fourth-order versions of ALESCAF and LEMACAF in various situations involving BPSK sources. Then, a performance study of LEMACAFC is carried out considering 4-QAM sources. These results show that the proposed algorithm provides satisfying estimations especially in the case of a large underdeterminacy level.
Descrição: ALMEIDA, A. L. F. de; LUCIANI, Xavier; COMON, Pierre. Blind identification of underdetermined mixtures based on the characteristic function: the complex case. IEEE Transactions on Signal Processing, [s.l.], v. 59, n. 2, 2011, p. 540-553
ISSN: 1053-587X
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