Friday 29th of March 2024
 

An Efficient Algorithm to Estimate Mixture Matrix in Blind Source Separation using Tensor Decomposition


Yan-Liang Zhang and Geng Li

The estimation of mixing matrix is a key step to solve the problem of blind source separation. The existing algorithm can only estimate the matrix of well-determined, over-determined and under-determined in condition of sparse source. Scaling and permutation ambiguities lie in both factor matrix of tensor Canonical Decomposition and mixing matrix in blind source separation. With this property, the estimation of mixing matrix can be transformed into tensor Canonical Decomposition of observed signals statistic. The decomposition can be realized by the method of alternating least squares. The theoretical analysis and simulations show that the method proposed in this paper is an efficient algorithm to estimate well-determined, over-determined and under-determined mixing matrix.

Keywords: Blind Source Separation(BSS); Tensor; Canonical Decomposition; Alternating Least Squares(ALS)

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ABOUT THE AUTHORS

Yan-Liang Zhang
Yan-liang Zhang received Doctor degree at Xidian University in 2011. She has been employed at Henan Polytechnic University since the summer of 2001. She has been supported by the National Natural Science Foundation of China. Her research fields are blind source separation and independent component analysis..

Geng Li
Geng Li born in 1982, Master of Engineering. . His research interests include wireless communication and Internet of things.


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