skip to main content
10.1145/3290480.3290509acmotherconferencesArticle/Chapter ViewAbstractPublication PagesiccnsConference Proceedingsconference-collections
research-article

A Modified Source Number Estimation Method based on Wavelet Analysis and Singular Value Decomposition

Published:02 November 2018Publication History

ABSTRACT

In underdetermined blind source separation (BSS) algorithms, source number estimation is a precondition and a difficulty. An improved source number estimation method based on wavelet analysis and singular value decomposition is pro-posed. First, the whitened observed signals are decomposed by wavelet analysis method. Next, the decomposed wavelet coefficients are reconstructed separately. Low frequency wavelet coefficients, the highest frequency wavelet coefficients, and original observed signals are chosen to form a new observed signals matrix. Then, covariance matrix of this new signal matrix is calculated, and eigenvalues of covariance matrix are calculated. Based on the obtained eigenvalues, a new calculation formula of judging threshold for source signals and noise is proposed. The proposed algorithm is validated by simulation.

References

  1. Kritchman, S. and Nadler, B. 2008. Determining the number of components in a factor model from limited noisy data. Chemometrics and Intelligent Laboratory Systems 94 (June 2008), 19--32.Google ScholarGoogle Scholar
  2. Murty, S. 1994. A technique to determine the number of incoherent sources to the response of a system. Mechanical System and Signal Processing 8, 4, 363--380.Google ScholarGoogle ScholarCross RefCross Ref
  3. Bofill, P., Zibulevsky M. 2001. Underdetermined blind source separation using sparse representations. Signal Processing 81(June 2001), 2353--2362.Google ScholarGoogle Scholar
  4. Mahmuddin, M. and Yusof, Y. 2010. Automatic estimation total number of cluster using a hybrid test-and-generate and K-means algorithm. International Conference on Computer Applications and Industrial Electronics (ICCAIE), 593--596.Google ScholarGoogle Scholar
  5. Tan, B. H., Zhao, M., and Xie, S. L. 2009. Sources number estimation and blind separation algorithm based on unsupervised learning. Systems Engineering and Electronics 31, 8 (Aug. 2009), 1790--1794.Google ScholarGoogle Scholar
  6. Jalil, T. and Nasser, M. 2012. A variational Bayes approach to the underdetermined blind source separation with automatic determination of the number of sources. IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 253--256.Google ScholarGoogle Scholar
  7. Zhao, X. Z. and Ye, B. Y. 2016. Singular value decomposition packet and its application to extraction of weak fault feature. Mechanical Systems and Signal Processing 70-71 (Sept. 2016), 73--86.Google ScholarGoogle Scholar
  8. Zuo, C. H., Zhang, J., and Chen, C. J. 2015. A Blind estimation method of source number in underdetermined mixing cases. Electronic Measurement Technology 38, 6 (June 2016), 113--117.Google ScholarGoogle Scholar

Index Terms

  1. A Modified Source Number Estimation Method based on Wavelet Analysis and Singular Value Decomposition

    Recommendations

    Comments

    Login options

    Check if you have access through your login credentials or your institution to get full access on this article.

    Sign in
    • Published in

      cover image ACM Other conferences
      ICCNS '18: Proceedings of the 8th International Conference on Communication and Network Security
      November 2018
      166 pages
      ISBN:9781450365673
      DOI:10.1145/3290480

      Copyright © 2018 ACM

      Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

      Publisher

      Association for Computing Machinery

      New York, NY, United States

      Publication History

      • Published: 2 November 2018

      Permissions

      Request permissions about this article.

      Request Permissions

      Check for updates

      Qualifiers

      • research-article
      • Research
      • Refereed limited

    PDF Format

    View or Download as a PDF file.

    PDF

    eReader

    View online with eReader.

    eReader