广义生成函数张量分解的欠定混合盲辨识

doi:10.3969/j.issn.1001-2400.2016.05.021

Abstract

Abstract:

Aimed at the problem of underdetermined blind identification, an algorithm based on generalized generating function decomposition is proposed, which no longer imposes sparsity restrictions on source signals. First, the second derivative matrices of the generalized generating function are stacked to the third-order tensor form, from which the number of source signals can be blindly estimated. Then the tensor is decomposed with singular value decomposition, and the mixture matrix is estimated by the joint diagonalization method. Simulation results validate the effectiveness of the proposed algorithm, and show that the proposed algorithm can acquire a better estimation precision than other classical algorithms with the same SNRs in the conditions of well-posed and underdetermined mixtures, meanwhile it extends the field of blind source separation application via the generalized generating function restricted only to the well-posed case.

Key words: underdetermined blind identification, general generating function, tensor decomposition, joint diagonalization, sparse component analysis

CLC Number:

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ZHOU Zhiwen;HUANG Gaoming;GAO Jun. Tensor decomposition of generalized generating function-based blind identification of underdetermined mixtures[J].Journal of Xidian University, 2016, 43(5): 116-120.

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Tensor decomposition of generalized generating function-based blind identification of underdetermined mixtures

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