التفاصيل البيبلوغرافية
| العنوان: |
CONFAC Decomposition Approach to Blind Identification of Underdetermined Mixtures Based on Generating Function Derivatives. |
| المؤلفون: |
de Almeida, André L. F., Luciani, Xavier, Stegeman, Alwin, Comon, Pierre |
| المصدر: |
IEEE Transactions on Signal Processing; Nov2012, Vol. 60 Issue 11, p5698-5713, 16p |
| مصطلحات موضوعية: |
MATHEMATICAL decomposition, CUMULANTS, COMPLEX variables, COMPUTATIONAL complexity, ESTIMATION theory |
| مستخلص: |
This work proposes a new tensor-based approach to solve the problem of blind identification of underdetermined mixtures of complex-valued sources exploiting the cumulant generating function (CGF) of the observations. We show that a collection of second-order derivatives of the CGF of the observations can be stored in a third-order tensor following a constrained factor (CONFAC) decomposition with known constrained structure. In order to increase the diversity, we combine three derivative types into an extended third-order CONFAC decomposition. A detailed uniqueness study of this decomposition is provided, from which easy-to-check sufficient conditions ensuring the essential uniqueness of the mixing matrix are obtained. From an algorithmic viewpoint, we develop a CONFAC-based enhanced line search (CONFAC-ELS) method to be used with an alternating least squares estimation procedure for accelerated convergence, and also analyze the numerical complexities of two CONFAC-based algorithms (namely, CONFAC-ALS and CONFAC-ELS) in comparison with the Levenberg-Marquardt (LM)-based algorithm recently derived to solve the same problem. Simulation results compare the proposed approach with some higher-order methods. Our results also corroborate the advantages of the CONFAC-based approach over the competing LM-based approach in terms of performance and computational complexity. [ABSTRACT FROM PUBLISHER] |
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| قاعدة البيانات: |
Complementary Index |
