基于改进粒子群的密度聚类算法混合矩阵估计

doi:10.12305/j.issn.1001-506X.2024.07.05

Abstract

Abstract:

Aiming at the problem that the traditional density-based spatial clustering of applications with noise (DBSCAN) algorithm in the mixing matrix estimation algorithm needs to artificially set the neighborhood radius and the number of core points, a double constrained particle swarm optimization (DCPSO) algorithm is proposed. The neighborhood radius parameters of the DBSCAN algorithm are optimized, and the obtained optimal parameters are used as the parameter input of the DBSCAN algorithm, and then the clustering center is calculated to complete the mixing matrix estimation. Aiming at the problem that the source signal number estimation algorithm based on distance sorting relies on the selection of empirical parameters and does not have the ability to eliminate noise points, a maximum distance sorting algorithm is proposed. The experimental results show that the improved algorithm is improved. The accuracy of source signal number estimation is nearly 40% higher than that of the original algorithm. The error of mixing matrix estimation is more than 3 dB higher than that of the comparison algorithm. Moreover, the proposed algorithm has a better convergence speed than the original algorithm.

Key words: underdetermined blind source separation, particle swarm optimization (PSO), density space clustering, mixing matrix estimation

CLC Number:

TN911.7

Chenghao LIU, Xiaolin ZHANG, Rongchen SUN, Ming LI. Estimation of mixture matrix of density clustering algorithm based on improved particle swarm optimization algorithm[J]. Systems Engineering and Electronics, 2024, 46(7): 2211-2219.

Figures/Tables 15

Fig.1

Fig.2

Fig.3

Fig.4

Fig.5

Fig.6

Fig.7

Fig.8

Fig.9

Fig.10

Fig.11

Fig.12

Fig.13

Table 1

Table 2

References 31

1	BOUSSÉ M , DEBALS O , LATHAUWER L D . A tensor-based method for large-scale blind source separation using segmentation[J]. IEEE Trans.on Signal Processing, 2017, 65 (2): 346- 358. doi: 10.1109/TSP.2016.2617858
2	MA B Z , ZHANG T Q , AN Z L , et al. Measuring dependence for permutation alignment in convolutive blind source separation[J]. IEEE Trans.on Circuits and Systems Ⅱ: Express Briefs, 2022, 69 (3): 1982- 1986. doi: 10.1109/TCSII.2021.3134716
3	LAWAL A , MAYYALA Q , ABED-MERAIM K , et al. Blind signal estimation using structured subspace technique[J]. IEEE Trans.on Circuits and Systems Ⅱ: Express Briefs, 2021, 68 (8): 3007- 3011. doi: 10.1109/TCSII.2021.3065385
4	JAMES C J , HESSE C W . Independent component analysis for biomedical signals[J]. Physiological Measurement, 2005, 26 (1): R15- R39. doi: 10.1088/0967-3334/26/1/R02
5	ZAHO Y C , FU W H , LIU Y Y . Energy correlation permutation algorithm of frequency-domain blind source separation based on frequency bins correction[J]. Wireless Personal Communications, 2021, 120 (2): 1753- 1768. doi: 10.1007/s11277-021-08533-w
6	CHEN J X , LIAO X , WANG W , et al. SNIS: a signal noise separation-based network for post-processed image forgery detection[J]. IEEE Trans.on Circuits and Systems for Video Technology, 2023, 33 (2): 935- 951. doi: 10.1109/TCSVT.2022.3204753
7	MIETTINEN J , NITZAN E , VOROBYOV S A , et al. Graph signal processing meets blind source separation[J]. IEEE Trans.on Signal Processing, 2021, 69, 2585- 2599. doi: 10.1109/TSP.2021.3073226
8	ZHANG L, LI C X, DENG F, et al. Multi-task audio source separation[C]//Proc. of the IEEE Automatic Speech Recognition and Understanding Workshop, 2021: 671-678.
9	XIE Y , XIE K , XIE S L . Underdetermined blind source separation of speech mixtures unifying dictionary learning and sparse representation[J]. International Journal of Machine Learning and Cybernetics, 2021, 12 (12): 3573- 3583. doi: 10.1007/s13042-021-01406-5
10	王川川, 曾勇虎. 欠定盲源分离算法的研究现状及展望[J]. 北京邮电大学学报, 2018, 41 (6): 103- 109.
	WANG C C , ZENG Y H . Research status and prospect of underdetermined blind source separation algorithm[J]. Journal of Beijing University of Posts and Telecommunications, 2018, 41 (6): 103- 109.
11	BOFILL P , ZIBULEVSKY M . Underdetermined blind source separation using sparse representations[J]. Signal Processing, 2001, 81 (11): 2353- 2362. doi: 10.1016/S0165-1684(01)00120-7
12	马宝泽, 李国军, 向翠玲, 等. 基于张量分析的欠定混合矩阵估计算法[J]. 通信学报, 2022, 43 (11): 35- 43. doi: 10.11959/j.issn.1000-436x.2022206
	MA B Z , LI G J , XIANG C L , et al. Underdetermined mixing matrix estimation algorithm based on tensor analysis[J]. Journal on Communications, 2022, 43 (11): 35- 43. doi: 10.11959/j.issn.1000-436x.2022206
13	LIEVEN D L , JOSÉPHINE C . Blind identification of under determined mixtures by simultaneous matrix diagonalization[J]. IEEE Trans.on Signal Processing, 2008, 56 (3): 1096- 1105. doi: 10.1109/TSP.2007.908929
14	LEE D D , SEUNG H S . Learning the parts of objects by non-negative matrix factorization[J]. Nature, 1999, 401 (6755): 788- 791. doi: 10.1038/44565
15	YANG S M , YI Z . Convergence analysis of non-negative matrix factorization for BSS algorithm[J]. Neural Processing Letters, 2010, 31 (1): 45- 64. doi: 10.1007/s11063-009-9126-0
16	WANG X B, WANG Y. A single-channel BSS new method of time-frequency overlapped communication signal based on multi-scale CWT and ICA[C]//Proc. of the International Conference on Computational Problem-Solving, 2013: 241-244.
17	刘方正, 韩振中, 曾瑞琪. 基于变分模态分解和压缩感知的弱观测条件下雷达信号重构方法[J]. 电子与信息学报, 2021, 43 (6): 1644- 1652.
	LIU F Z , HAN Z Z , ZENG R Q . Damaged radar signal reconstruction method based on variational mode decomposition and compressed sensing[J]. Journal of Electronics & Information Technology, 2021, 43 (6): 1644- 1652.
18	FU W H . Mixing matrix estimation algorithm for underdetermined blind source separation[J]. IEEE Access, 2021, 9, 136284- 136291. doi: 10.1109/ACCESS.2021.3114169
19	LU J T , WEI C , ZI Y Y . A novel underdetermined blind source separation method and its application to source contribution quantitative estimation[J]. Sensors, 2019, 19 (6): 1413. doi: 10.3390/s19061413
20	GAO H Y , ZHANG Z W , WANG S H , et al. Underdetermined blind source separation method based on quantum archimedes optimization algorithm[J]. Applied Intelligence, 2022, 53 (11): 13763- 13800.
21	ABRARD F , DEVILLE Y . A time-frequency blind signal separation method applicable to underdetermined mixtures of dependent sources[J]. Signal Processing, 2005, 85 (7): 1389- 1403. doi: 10.1016/j.sigpro.2005.02.010
22	MA B Z , ZHANG T Q . Underdetermined blind source separation based on source number estimation and improved sparse component analysis[J]. Circuits, Systems, and Signal Processing, 2021, 40 (7): 3417- 3436. doi: 10.1007/s00034-020-01629-x
23	KIM S G , YOO C D . Underdetermined blind source separation based on subspace representation[J]. IEEE Trans.on signal processing, 2009, 57 (7): 2604- 2614. doi: 10.1109/TSP.2009.2017570
24	ESTE M , KRIEGEL H P , SANDER J , et al. A density-based algorithm for discovering clusters in large spatial databases with noise[J]. Association for Computing Machinery, 1996, 96 (5): 226- 231.
25	张宇, 杨淇善, 贾懋珅. 利用DBSCAN和概率密度估计的欠定盲源分离混合矩阵估计[J]. 信号处理, 2023, 39 (4): 708- 718.
	ZHANG Y , YANG Q S , JIA M S . Mixing matrix estimation using DBSCAN and probability density estimation for underdetermined blind source separation[J]. Journal of Signal Processing, 2023, 39 (4): 708- 718.
26	TANG J , LIU G , PAN Q . A review on representative swarm intelligence algorithms for solving optimization problems: applications and trends[J]. IEEE/CAA Journal of Automatica Sinica, 2021, 8 (10): 1627- 1643. doi: 10.1109/JAS.2021.1004129
27	XIE Y , XIE K , XIE S L . Underdetermined blind separation of source using lp-norm diversity measures[J]. Neurocomputing, 2020, 29 (411): 259- 267.
28	季策, 穆文欢, 耿蓉. 基于A-DBSCAN的欠定盲源分离算法[J]. 系统工程与电子技术, 2020, 42 (12): 2676- 2683. doi: 10.3969/j.issn.1001-506X.2020.12.02
	JI C , MU W H , GENG R . Underdetermined blind source separation algorithm based on A-DBSCAN[J]. Systems Engineering and Electronics, 2020, 42 (12): 2676- 2683. doi: 10.3969/j.issn.1001-506X.2020.12.02
29	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 mixing matrix estimation based on DBSCAN and CFSFDP[J]. Journal of Data Acquisition and Processing, 2019, 34 (5): 819- 830.
30	VINCENT E , GRIBONVAL R , FEVOTTE C . Performance measurement in blind audio source separation[J]. IEEE Trans.on Audio, Speech and Language Processing, 2006, 14 (4): 1462- 1469. doi: 10.1109/TSA.2005.858005
31	GUO F Y, ZHANG Y, GONG D W, et al. A cooperative pa-rallel mechanism based multi-particle-swarm optimizer[C]//Proc. of the 7th World Congress on Intelligent Control and Automation, 2008: 8749-8754.

SNR	SIR	SNR	SIR
0	-0.008	20	12.585
5	5.052	25	13.089
10	8.627	30	13.661
15	10.986	35	13.923

SNR/dB	γ/%	SNR/dB	SIR/dB
0	63.33	20	96.45
5	84.76	25	96.66
10	92.54	30	96.93
15	95.23	35	96.94

[1]	Cancan TAO, Rui ZHOU. A method of UAV motion control to optimize air-ground relay network [J]. Systems Engineering and Electronics, 2024, 46(5): 1712-1723.
[2]	Yujie LIU, Kaikai CUI, Wei HAN, Yue LI. Research on departure planning of carrier aircraft based on IPSO [J]. Systems Engineering and Electronics, 2024, 46(4): 1337-1345.
[3]	Yu MO, Xudong TANG. Research on mechanical resonance suppression of electric servo system based on an improved ADRC [J]. Systems Engineering and Electronics, 2024, 46(1): 309-317.
[4]	Ying ZENG, Yanfeng LI, Hongyi WANG, Huaming QIAN, Hongzhong HUANG. Reliability analysis of industrial robot driver combining MRGP and PSO [J]. Systems Engineering and Electronics, 2023, 45(8): 2643-2650.
[5]	Leilei ZHANG, Xi LIU, Xulin LIU, Hongjun YANG, Feng ZHANG. Accelerated storage life assessment method under zero-failure data for electromechanical products of missile [J]. Systems Engineering and Electronics, 2023, 45(7): 2287-2294.
[6]	Junfeng SUN, Chenghai LI, Bo CAO. Network security situation prediction based on TCN-BiLSTM [J]. Systems Engineering and Electronics, 2023, 45(11): 3671-3679.
[7]	Shengyang HE, Jiepeng DU, Yaqin ZHAO, Baoying WANG, Liang ZHAO, Longwen WU. TDOA-based cooperative single target location using UAV cluster [J]. Systems Engineering and Electronics, 2023, 45(1): 1-8.
[8]	Jianfeng YANG, Heye XIAO, Liang LI, Junqiang BAI, Weihao DONG. Multi-level module partition method of UAV based on fuzzy clustering and expert scoring mechanism [J]. Systems Engineering and Electronics, 2022, 44(8): 2530-2539.
[9]	Pengyu CAO, Chengzhi YANG, Limeng SHI, Hongchao WU. Unknown radar signal processing based on PSO-DBSCAN and SCGAN [J]. Systems Engineering and Electronics, 2022, 44(4): 1158-1165.
[10]	Siyu DU, Yinghui QUAN, Minghui SHA, Wen FANG, Mengdao XING. Waveform optimization for SFA radar based on evolutionary particle swarm optimization [J]. Systems Engineering and Electronics, 2022, 44(3): 834-840.
[11]	Chenrui SHI, Lu TIAN, Zhan XU, Ruxin ZHI, Jinhui CHEN. Effectiveness evaluation method of emergency communication and sensing equipment based on PSO-BP [J]. Systems Engineering and Electronics, 2022, 44(11): 3455-3462.
[12]	Fuyu LU, Ningning TONG, Weike FENG, Pengcheng WAN. Adaptive hybrid annealing particle swarm optimization algorithm [J]. Systems Engineering and Electronics, 2022, 44(11): 3470-3476.
[13]	Haoyang LI, Jianjun XIANG, Fang PENG, Shuai WANG, Zhijun LI. Beam space generalized sidelobe canceller algorithm based on particle swarm optimization [J]. Systems Engineering and Electronics, 2022, 44(10): 3037-3045.
[14]	Mengchen ZHAO, Xiujuan YAO, Jing WANG, Suhui DONG. Single-channel blind source separation method of spatial aliasing signal based on Stacked-TCN [J]. Systems Engineering and Electronics, 2021, 43(9): 2628-2636.
[15]	Kun WANG, Shuxian HOU, Li WANG. APU performance parameter prediction model based on adaptive variation PSO-SVM [J]. Systems Engineering and Electronics, 2021, 43(2): 526-536.

Estimation of mixture matrix of density clustering algorithm based on improved particle swarm optimization algorithm

RichHTML

PDF (PC)

Knowledge

Abstract

Cite this article

share this article

Figures/Tables 15

References 31

Related Articles 15

Recommended Articles

Metrics

Comments