一种最大密度检测欠定混合矩阵估计算法

doi:10.19665/j.issn1001-2400.2019.01.017

Abstract

Abstract:

Aiming at mixing matrix estimation when the source number is unknown for underdetermined blind source separation (UBSS), a mixing matrix estimation method based on maximum density point searching is proposed. Based on sparse representation of observed signals, for the proposed algorithm, preprocessing of observed signals is processed first, and then the maximum density point of each observed signal is searched, after which the effective sample points are assembled, and then the source number and mixing matrix are estimated by the clustering method. For validation of the proposed algorithm, the simulations are developed by employing two sparse representation methods, which are single source point detection in the time-frequency domain and wavelet transform. Results show that the source number and the mixing matrix effect of the proposed algorithm are better than those of the reference algorithm, and that the calculation complexity of the proposed algorithm is much less than that of the reference algorithm. Further tests show that the proposed algorithm is applicable for mixing matrix estimation of positive-determined, overdetermined and underdetermined blind source separation models.

Key words: underdetermined blind source separation, sparse representation, maximum density point searching algorithm, source number estimation, mixing matrix estimation

CLC Number:

TN911

WANG Chuanchuan,ZENG Yonghu,FU Weihong,WANG Liandong. Estimation algorithm for an underdetermined mixing matrix based on maximum density point searching[J].Journal of Xidian University, 2019, 46(1): 106-111.

Figures/Tables 5

References 12

[1]	KIM S G, YOO C D . Underdetermined Blind Source Separation Based on Subspace Representation[J]. IEEE Transactions on Signal Processing, 2009,57(7):2604-2614. doi: 10.1109/TSP.2009.2017570
[2]	毕晓君, 宫汝江 . 基于混合聚类和网格密度的欠定盲矩阵估计[J]. 系统工程与电子技术, 2012,34(3):614-618. doi: 10.3969/j.issn.1001-506X.2012.03.34
	BI Xiaojun, GONG Rujiang . Underdetermined Blind Mixing Matrix Estimation Algorithm Based on Mixing Clustering and Mesh Density[J]. Systems Engineering and Electronics, 2012,34(3):614-618. doi: 10.3969/j.issn.1001-506X.2012.03.34
[3]	KIRBIZ S , GÜNSEL B. Perceptually Enhanced Blind Single-channel Music Source Separation by Non-negative Matrix Factorization[J]. Digital Signal Processing: a Review Journal, 2013,23(2):646-658. doi: 10.1016/j.dsp.2012.10.001
[4]	付卫红, 马丽芬, 李爱丽 . 基于改进K-均值聚类的欠定混合矩阵盲估计[J]. 系统工程与电子技术, 2014,36(11):2143-2148. doi: 10.3969/j.issn.1001-506X.2014.11.06
	FU Weihong, MA Lifen, LI Aili . Blind Estimation of Underdetermined Mixing Matrix Based on Improved K-means Clustering[J]. Systems Engineering and Electronics, 2014,36(11):2143-2148. doi: 10.3969/j.issn.1001-506X.2014.11.06
[5]	CONG F Y, LIN Q H, KUANG L D , et al. Tensor Decomposition of EEG Signals: a Brief Review[J]. Journal of Neuroscience Methods, 2015,248:59-69. doi: 10.1016/j.jneumeth.2015.03.018 pmid: 25840362
[6]	LI Y B, NIE W, YE F , et al. A Mixing Matrix Estimation Algorithm for Underdetermined Blind Source Separation[J]. Circuits, Systems, and Signal Processing, 2016,35(9):3367-3379. doi: 10.1007/s00034-015-0198-y
[7]	ZOU L, CHEN X, JI X Y , et al. Underdetermined Joint Blind Source Separation of Multiple Datasets[J]. IEEE Access, 2017,5:7474-7487. doi: 10.1109/ACCESS.2017.2695497
[8]	WANG C C, XU J Q, ZENG Y H. Research on Influence of Source Number Estimation on Application of Blind Source Separation Algorithms [C]// Procedia Computer Science: 107. Amsterdam: Elsevier B V, 2017: 379-384.
[9]	SU Q, SHEN Y H, WEI Y M , et al. Underdetermined Blind Source Separation by a Novel Time-frequency Method[J]. International Journal of Electronics and Communications (AEü), 2017,77:43-49. doi: 10.1016/j.aeue.2017.04.025
[10]	刘明骞, 陈钱, 吴芸 , 等. Underlay认知下时频重叠信号的分量个数估计[J]. 西安电子科技大学学报, 2017,44(6):43-47. doi: 10.3969/j.issn.1001-2400.2017.06.008
	LIU Mingqian, CHEN Qian, WU Yun , et al. Component Number Estimation of Time-frequency Overlapped Signals in Underlay Cognitive Radio[J]. Journal of Xidian University, 2017,44(6):43-47. doi: 10.3969/j.issn.1001-2400.2017.06.008
[11]	YANG Z, CHEN W T, HUANG J . Enhancing Recommendation on Extremely Sparse Data with Blocks-coupled Non-negative Matrix Factorization[J]. Neurocomputing, 2018,278:126-133. doi: 10.1016/j.neucom.2017.04.080
[12]	TASESKA M, HABETS E A P . Blind Source Separation of Moving Sources Using Sparsity-based Source Detection and Tracking[J]. IEEE/ACM Transactions on Audio, Speech, and Language Processing, 2018,26(3):657-670. doi: 10.1109/TASLP.2017.2780993

信噪比/dB	M
信噪比/dB	0.2	0.4	0.6	0.8	1.0
3	0	0	0	4.0	9.0
6	0	0	0	8.5	19.0
9	2.0	7.5	50.5	74.0	76.0
12	78.5	90.0	91.0	92.0	79.0
15	94.0	94.5	94.0	91.5	87.5
18	96.5	98.5	93.5	94.0	82.0
21	100	100	99.0	98.0	93.5

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Estimation algorithm for an underdetermined mixing matrix based on maximum density point searching

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