基于张量分析的欠定混合矩阵估计算法

doi:10.11959/j.issn.1000-436x.2022206

Journal on Communications ›› 2022, Vol. 43 ›› Issue (11): 35-43.doi: 10.11959/j.issn.1000-436x.2022206

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Underdetermined mixing matrix estimation algorithm based on tensor analysis

Baoze MA¹^,²^,³, Guojun LI¹^,², Cuiling XIANG¹^,², Yang XU¹^,²

¹ School of Electro-optics Engineering, Chongqing University of Posts and Telecommunications, Chongqing 400065, China
² Lab of Beyond LOS Reliable Information Transmission, Chongqing University of Posts and Telecommunications, Chongqing 400065, China
³ Postdoctoral Research Workstation of Chongqing Key Laboratory of Optoelectronic Information Sensing and Transmission Technology, Chongqing University of Posts and Telecommunications, Chongqing 400065, China

Revised:2022-10-09 Online:2022-11-25 Published:2022-11-01
Supported by:
The National Key Research and Development Program of China(2019YFC1511300);The National Natural Science Foundation of China(62201113);Chongqing Key Research and Development Program(cstc2017zdcy-zdyfX0011)

Abstract

Abstract:

Aiming at the problems of difficult to extract effective feature information and the slow convergence speed of the underdetermined matrix estimation, an underdetermined matrix estimation algorithm of instantaneous mixtures based on tensor analysis was proposed to overcome the constraint of signal sparsity.In the proposed algorithm, the symmetric third-order tensor was constructed via the autocovariance matrix of segmentation sub-block, which was compressed into a kernel tensor to reduce the size of the data.An enhanced line search technology was applied to speed up the convergence of alternating least squares method, and the factor matrix was used as the measure of the mixing matrix estimation, but the selection of the number of segmentation sub-blocks was an open problem.Experimental results demonstrate that the proposed algorithm outperforms the sparse transformation method and the traditional high-order statistical method in handling the underdetermined mixing matrix estimation.

Key words: underdetermined matrix estimation, symmetric tensor, segmentation strategy, autocovariance matrix

CLC Number:

TN911.6

Baoze MA, Guojun LI, Cuiling XIANG, Yang XU. Underdetermined mixing matrix estimation algorithm based on tensor analysis[J]. Journal on Communications, 2022, 43(11): 35-43.

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References 21

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算法	混合矩阵(m×n)平均相对误差/dB
算法	3×4	4×5	5×6
LI-TIFROM	-25.85	-20.23	-15.40
UBSS-SCA	-14.87	-13.79	-14.76
SOBIUM	-26.05	-20.86	-18.25
本文算法	-29.34	-25.17	-19.49

Underdetermined mixing matrix estimation algorithm based on tensor analysis

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