Mixing Matrix Estimation Algorithm for Underdetermined Instantaneous Mixing Model
Volume 15, Number 1, January 2019, pp. 337-345 DOI: 10.23940/ijpe.19.01.p34.337345
Fan Shia and Chuang Liub
aFaculty of Maritime and Transportation, Ningbo University, Ningbo, 315211, China bCollege of Information and Communication Engineering, Harbin Engineering University, Harbin, 150001, China (Submitted on November 7, 2018; Revised on December 3, 2018; Accepted on January 3, 2019)
Abstract:
Estimating the mixing matrix is a research that focuses on underdetermined blind source separation. In order to get a more accurate estimated mixing matrix, we investigate a novel algorithm for mixing matrix estimation. Firstly, a new method for detecting single source points was introduced. Then, we reckoned the signal quantity and initial clustering centers by adopting an improved clustering method based on the potential function. Finally, the point density theory and the initial clustering centers were utilized to get more accurate clustering centers and estimated the mixing matrix. The simulation results illustrate that we can obtain the more accurate and stable estimation of the mixing matrix by using the proposed algorithm.
References: 22
- J. J. Yang and H. L. Liu, “Blind Identification of the Underdetermined Mixing Matrix based on K-Weighted Hyperline Clustering,” Neurocomputing, Vol. 149, No. PB, pp. 483-489, 2015
- M. T. Huang, C. H. Lee, and C. M. Lin, “Blind Source Separation with Adaptive Learning Rates for Image Encryption,” Journal of Intelligent and Fuzzy Systems, Vol. 30, No. 1, pp. 451-460, 2015
- J. Liu, S. E. Yang, S. C. Piao, and Y. W. Huang, “Blind Source Separation of Ship-Radiated Noise using Single Observing Channel,” ACTA ACUSTICA, Vol. 36, No. 3, pp. 265-270, 2011
- Z. C. Shan, C. S. Lin, and Q. Xiang, “The Separation of Ship Signal based on Second Order Nonstationary Statistic,” SIGNAL PROCESSING, Vol. 25, No. 6, pp. 973-976, 2009
- F. Andreotti, J. Behar, S. Zaunseder, J. Oster, and G. D. Clifford, “An Open-Source Framework for Stress-Testing Non-Invasive Foetal ECG Extraction Algorithms,” Physiological Measurement, Vol. 37, No. 5, pp. 627-648, 2016
- S. Farashi, “Spike Detection using a Multiresolution Entropy based Method,” Biomedical Engineering-Biomedizinische Technik, Vol. 63, No. 4, pp. 361-376, 2018
- M. G. S. Sriyananda, J. Joutsensalo, and T. Hamalainen, “Blind Source Separation based Interference Suppression Schemes for OFDM and DS-CDMA,” Telecommunication Systems, Vol. 4, No. 2, pp. 1-10, 2016
- Z. C. Sha, Z. T. Huang, Y. Y. Zhou, and F. H. Wang, “Frequency-Hopping Signals Sorting based on Underdetermined Blind Source Separation,” IET Communications, Vol. 7, No. 14, pp. 1456-1464, 2013
- G. R. Naik and D. K. Kumar, “An Overview of Independent Component Analysis and its Applications,” International Journal of Computing and Information Sciences, Vol. 35, No. 1, pp. 63-81, 2011
- X. He, F. He, and T. Zhu, “Large-Scale Super-Gaussian Sources Separation using Fast-ICA with Rational Nonlinearities,” International Journal of Adaptive Control and Signal Processing, Vol. 31, No. 3, pp. 379-397, 2017
- S. Qin, J. Guo, and C. Zhu, “Sparse Component Analysis using Time-Frequency Representations for Operational Modal Analysis,” Sensors, Vol. 15, No. 3, pp. 6497-6519, 2015
- Y. Zhong, X. Wang, L. Zhao, R. Y. Feng, L. P. Zhang, and Y. Y. Xu, “Blind Spectral Unmixing based on Sparse Component Analysis for Hyperspectral Remote Sensing Imagery,” Isprs Journal of Photogrammetry and Remote Sensing, Vol. 119, pp. 49-63, 2016
- Y. Li, S. I. Amari, A. Cichocki, D. W. C. Ho, and S. L. Xie, “Underdetermined Blind Source Separation based on Sparse Representation,” IEEE Transactions on Signal Processing, Vol. 54, No. 2, pp. 423-437, 2006
- O. Yilmaz and S. Rickard, “Blind Separation of Speech Mixtures Via Time-Frequency Masking,” IEEE Transactions on Signal Processing, Vol. 52, No. 7, pp. 1830-1847, 2004
- A. Aissa-El-Bey, N. Linh-Trung, K. Abed-Meraim, A. Belouchrani, and Y. Grenier, “Underdetermined Blind Separation of Nondisjoint Sources in the Time-Frequency Domain,” IEEE Transactions on Signal Processing, Vol. 55, No. 3, pp. 897-907, 2007
- J. Thiagarajan, K. Natesan Ramamurthy, and A. Spanias, “Mixing Matrix Estimation using Discriminative Clustering for Blind Source Separation,” Digital Signal Processing, Vol. 23, No. 1, pp. 9-18, 2013
- F. C. Feng and M. Kowalski, “Revisiting Sparse ICA from a Synthesis Point of View: Blind Source Separation for over and Underdetermined Mixtures,” Signal Processing, Vol. 152, pp. 165-177, 2018
- F. Abrard and Y. Deville, “A Time-Frequency Blind Signal Separation Method Applicable to Underdetermined Mixtures of Dependent Sources,” Signal Processing, Vol. 85, No. 7, pp. 1389-1403, 2005
- Q. Guo and G. Q. Ruan, “A Complex-Valued Mixing Matrix Estimation Algorithm for Underdetermined Blind Source Separation,” Circuits Systems and Signal Processing, Vol. 37, No. 8, pp. 3206-3226, 2018
- F. Lu, Z. Huang, and W. Jiang, “Underdetermined Blind Separation of Non-Disjoint Signals in Time-Frequency Domain based on Matrix Diagonalization,” Signal Processing, Vol. 91, No. 7, pp. 1568-1577, 2011
- V. G. Reju, S. N. Koh, and I. Y. Soon, “An Algorithm for Mixing Matrix Estimation in Instantaneous Blind Source Separation,” Signal Processing, Vol. 89, No. 9, pp. 1762-1773, 2009
- T. Dong, Y. Lei, and J. Yang, “An Algorithm for Underdetermined Mixing Matrix Estimation,” Neurocomputing, Vol. 104, pp. 26-34, 2013
Please note : You will need Adobe Acrobat viewer to view the full articles. |