基于A-DBSCAN的欠定盲源分离算法

doi:10.3969/j.issn.1001-506X.2020.12.02

Abstract

Abstract:

In order to improve the estimation accuracy of the mixing matrix in the underdetermined blind source separation, an adaptive density-based spatial clustering of applications with noise (A-DBSCAN) algorithm is proposed, which is based on the DBSCAN algorithm. To solve the problem that the neighborhood radius (Eps) and the number of neighborhood points (MinPts) of the DBSCAN algorithm are determined, First, the curve fitting method is used to obtain Eps, then MinPts is determined by analyzing the relationship between the number of cluster output categories and the number of noise points. The proposed algorithm is combined with the mixing matrix estimation model, and finally the source signal recovery is achieved by the shortest path algorithm. Experimental results show that compared with the comparison algorithm, the proposed algorithm has significantly improved the performance of mixing matrix estimation and source signal recovery.

Key words: underdetermined blind source separation, density clustering, curve fitting, neighborhood radius, neighborhood point

CLC Number:

TN911.7

Ce JI, Wenhuan MU, Rong GENG. Underdetermined blind source separation algorithm based on A-DBSCAN[J]. Systems Engineering and Electronics, 2020, 42(12): 2676-2683.

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References 25

1	BERTRAND A , MOONEN M . Distributed canonical correlation analysis in wireless sensor networks with application to distributed blind source separation[J]. IEEE Trans.on Signal Proces-sing, 2015, 63 (18): 4800- 4813. doi: 10.1109/TSP.2015.2443729
2	YE F , CHEN J , GAO L P , et al. A mixing matrix estimation algorithm for the time-delayed mixing model of the underdetermined blind source separation problem[J]. Circuits Systems and Signal Processing, 2019, 38 (4): 1889- 1906. doi: 10.1007/s00034-018-0930-5
3	ZHANG L J, YE Z, ZHANG Y, et al. Underdetermined mixing matrix estimation based on single source detection[C]//Proc.of the China International SAR Symposium, 2018.
4	KARIM D , SALAH H , ADNEN C , et al. Hybridization DE with K-means for speaker clustering in speaker diarization of broadcasts news[J]. International Journal of Speech Technology, 2019, 22 (4): 893- 909. doi: 10.1007/s10772-019-09633-6
5	GALAN S F . Comparative evaluation of region query strategies for DBSCAN clustering[J]. Information Sciences, 2019, 502, 76- 90. doi: 10.1016/j.ins.2019.06.036
6	LIN C Y , CHANG C C , LIN C C . A new density-based scheme for clustering based on genetic algorithm[J]. Fundamental Information, 2005, 68 (4): 315- 331.
7	杨静, 高嘉伟, 梁吉业. 基于数据场的改进DBSCAN聚类算法[J]. 计算机科学与探索, 2012, 6 (10): 903- 911.
	YANG J , GAO J W , LIANG J Y . Improved DBSCAN clustering algorithm based on data field[J]. Computer Science and Exploration, 2012, 6 (10): 903- 911.
8	冯振华, 钱雪忠, 赵娜娜. Greedy DBSCAN:一种针对多密度聚类的DBSCAN改进算法[J]. 计算机应用研究, 2016, 33 (9): 2693- 2696, 2700.
	FENG Z H , QIAN X Z , ZHAO N N . Greedy DBSCAN: an improved DBSCAN algorithm for multi-density clustering[J]. Computer Application Research, 2016, 33 (9): 2693- 2696, 2700.
9	TOHTAYONG M, KHAN S, YAACOB M, et al. Harmonics elimination based on curve fitting method for modular multilevel converters[C]//Proc.of the IEEE Region 10 Conference, 2017: 1890-1895.
10	LI Y N, PENG W B, ZHU X Y, et al. Based on short-time fourier transform impulse frequency response analysis in the application of the transformer winding deformation[C]//Proc.of the IEEE International Conference on Mechatronics and Automation, 2016: 371-375.
11	任喜顺, 沈越泓, 高猛, 等. 基于时频分析的混合矩阵估计方法[J]. 信号处理, 2012, 28 (4): 545- 553.
	REN X S , SHEN Y H , GAO M , et al. Mixing matrix estimation method based on time frequency analysis[J]. Signal Processing, 2012, 28 (4): 545- 553.
12	GUO Q , RUAN G Q , NAN P L . Underdetermined mixing matrix estimation algorithm based on single source points[J]. Circuits Systems and Signal Processing, 2017, 36 (11): 4453- 4467. doi: 10.1007/s00034-017-0522-9
13	谭北海, 赵敏, 谢胜利. 基于无监督学习的源数估计及盲分离算法[J]. 系统工程与电子技术, 2009, 31 (8): 1790- 1794.
	TAN B H , ZHAO M , XIE S L . Source number estimation and blind separation algorithm based on unsupervised learning[J]. Systems Engineering and Electronics, 2009, 31 (8): 1790- 1794.
14	BRYANT A C , CIOS K J . RNN-DBSCAN: a density-based clustering algorithm using reverse nearest neighbor density estimates[J]. IEEE Trans.on Knowledge and Data Engineering, 2018, 30 (6): 1109- 1121. doi: 10.1109/TKDE.2017.2787640
15	SUN J D , LI Y X , WEN J T , et al. Novel mixing matrix estimation approach in underdetermined blind source separation[J]. Neurocomputing, 2016, 173, 623- 632. doi: 10.1016/j.neucom.2015.08.008
16	董耀刚, 颉丽, 姚正学, 等. 基于傅里叶函数拟合的滑坡变形监测分析[J]. 中国水土保持, 2019, 11, 72- 75.
	DONG Y G , JIE L , YAO Z X , et al. Monitoring analysis of landslide deformation based on Fourier function fitting[J]. Soil and Water Conservation in China, 2019, 11, 72- 75.
17	WANG R, HUANG R, GAO Y, et al. Sampling method for GMM curve fitting in multi object tracking[C]//Proc.of the IEEE International Conference on Imaging Systems and Techniques, 2017.
18	张永涛, 贾延明. 最小二乘法中代数多项式曲线拟合的分析及实现[J]. 计算机与数字工程, 2017, 45 (4): 637- 639, 654.
	ZHANG Y T , JIA Y M . Analysis and realization of algebraic polynomial curve fitting in least square method[J]. Computer and Digital Engineering, 2017, 45 (4): 637- 639, 654.
19	周治平, 王杰锋, 朱书伟, 等. 一种改进的自适应快速AF-DBSCAN聚类算法[J]. 智能系统学报, 2016, 11 (1): 93- 98.
	ZHOU Z P , WANG J F , ZHU S W , et al. An improved adaptive fast AF-DBSCAN clustering algorithm[J]. Journal of Intelligent Systems, 2016, 11 (1): 93- 98.
20	何选森, 何帆. 密度空间与密度峰值聚类的欠定混合矩阵估计[J]. 数据采集与处理, 2019, 34 (5): 819- 830.
	HE X S , HE F . Underdetermined mixed matrix estimation of density space and density peak clustering[J]. Data Acquisition and Processing, 2019, 34 (5): 819- 830.
21	LYU S, LIU C C, ZHANG C, et al. Underdetermined blind speech signal separation method based on the improved shortest path method[C]//Proc.of the International Conference on Swarm Intelligence, 2015: 397-404.
22	TAHERI N, KACHENOURA A, ANSARIASL K, et al. Feasibility of blind source separation methods for the denoising of dense-array EEG[C]//Proc.of the IEEE 37th Annual International Conference of the Engineering in Medicine & Biology Society, 2015: 4773-4776.
23	ZHEN L , PENG D , YI Z , et al. Underdetermined blind source separation using sparse coding[J]. IEEE Trans.on Neural Networks and Learning Systems, 2017, 28 (12): 3102- 3108. doi: 10.1109/TNNLS.2016.2610960
24	WANG W, SU D L, ZHAO Z H. Mixing matrix estimation in blind source separation based on CFSFDP[C]//Proc.of the International Applied Computational Electromagnetics Society Symposium, 2019.
25	LEI X J , DING Y L , WU F X . Detecting protein complexes from DIPNs by density based clustering with pigeon-inspired optimization algorithm[J]. Science China Information Sciences, 2016, 59 (7): 070103. doi: 10.1007/s11432-016-5578-9

算法	Eps	MinPts	聚类数	F值
DBSCAN	0.005	4	6	0.727 6
	0.010	4	4	0.738 4
	0.015	4	3	0.899 2
A-DBSCAN	0.007	8	3	0.953 7

实验2	混合矩阵	NMSE/dB
算法1	$\left[ \begin{array}{l}\begin{array}{{20}{c}}{0.441\;5}&{ - 0.725\;1}&{0.924\;0}\end{array}\\\begin{array}{{20}{c}}{0.897\;2}&{\;\;0.688\;6}&{\;0.382\;4}\end{array}\end{array} \right]$	-31.827 6
算法2	$\left[ \begin{array}{l}\begin{array}{{20}{c}}{0.407\;7}&{ - 0.712\;0}&{0.926\;8}\end{array}\\\begin{array}{{20}{c}}{0.913\;1}&{\;\;0.702\;2}&{\;0.375\;5}\end{array}\end{array} \right]$	-46.732 6
算法3	$\left[ {\begin{array}{{20}{l}}{\begin{array}{{20}{c}}{0.409\;0}&{ - 0.708\;2}&{0.926\;9}\end{array}}\\{\begin{array}{*{20}{c}}{0.912\;5}&{\;\;0.706\;0}&{\;0.375\;2}\end{array}}\end{array}} \right]$	-55.314 3

实验2	算法1	算法2	算法3
源信号1	0.906 2	0.911 4	0.911 7
源信号2	0.956 8	0.959 5	0.959 8
源信号3	0.968 9	0.971 2	0.971 4

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[3]	TENG Jiong-hua, XU Jing-lin, LU Long, ZHOU San-ping, HAN Jun-wei. Non-local image filter algorithm based on adaptive parameter regression [J]. Systems Engineering and Electronics, 2015, 37(2): 449-454.
[4]	BI Xiao-jun, GONG Ru-jiang. Underdetermined blind mixing matrix estimation algorithm based on mixing clustering and mesh density [J]. Journal of Systems Engineering and Electronics, 2012, 34(3): 614-618.
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Underdetermined blind source separation algorithm based on A-DBSCAN

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