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

被引：0
|
作者：
Liu C. [1 ]
Zhang X. [1 ]
Sun R. [1 ]
Li M. [1 ]
机构：
[1] College of Information and Communication Engineering, Harbin Engineering University, Harbin
来源：
Xi Tong Gong Cheng Yu Dian Zi Ji Shu/Systems Engineering and Electronics | 2024年 / 46卷 / 07期
关键词：
density space clustering; mixing matrix estimation; particle swarm optimization (PSO); underdetermined blind source separation;
D O I：
10.12305/j.issn.1001-506X.2024.07.05
中图分类号：
学科分类号：
摘要：
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. © 2024 Chinese Institute of Electronics. All rights reserved.
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页码：2211 / 2219
页数：8
相关论文
共 31 条
  • [1] BOUSSE M, DEBALS O, LATHAUWER L D., A tensor-based method for large-scale blind .source separation using segment at ion, IEEE Trans, on Signal Processing, 65, 2, pp. 346-358, (2017)
  • [2] MA B Z, ZHANG T Q, AN Z L, Et al., Measuring dependence for permutation alignment in convolutive blind source separation, IEEE Trans, on Circuits and Systems II: Express Briefs, 69, 3, pp. 1982-1986, (2022)
  • [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, 68, 8, pp. 3007-3011, (2021)
  • [4] JAMES C J, HESSE C W., Independent component analysis for biomedical signals[j], Physiological Measurement, 26, 1, pp. R15-R39, (2005)
  • [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, Wireless Personal Communications, 120, 2, pp. 1753-1768, (2021)
  • [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, 33, 2, pp. 935-951, (2023)
  • [7] MIETTINEN J, NITZAN E, VOROBYOV S A, Et al., Graph signal processing meets blind source separation [J], IEEE Trans, on Signal Processing, 69, pp. 2585-2599, (2021)
  • [8] ZHANG L, LI C X, DENG F, Et al., Multi-task audio source separation, Proc. of the IEEE Automatic Speech Recognition and Understanding Workshop, pp. 671-678, (2021)
  • [9] XIE Y, XIE K, XIE S L., Under.tion of speech mixtures unifying representation [J], International and Cybernetics, 12, 12, (2021)
  • [10] WANG C C, ZENG Y H., Research status and prospect of underdetermined blind source separation algorithm, Journal of Beijing University of Posts and Telecommunications, 41, 6, pp. 103-109, (2018)
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