Username   Password       Forgot your password?  Forgot your username? 


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)


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

      1. 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
      2. 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
      3. 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
      4. 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
      5. 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
      6. S. Farashi, “Spike Detection using a Multiresolution Entropy based Method,” Biomedical Engineering-Biomedizinische Technik, Vol. 63, No. 4, pp. 361-376, 2018
      7. 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
      8. 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
      9. 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
      10. 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
      11. 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
      12. 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
      13. 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
      14. 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
      15. 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
      16. 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
      17. 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
      18. 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
      19. 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
      20. 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
      21. 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
      22. 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.Get Free Adobe Reader

          This site uses encryption for transmitting your passwords.