Skip to main content
Springer Nature Link
Log in

Using Hierarchical Filters to Detect Sparseness in Unknown Channels

  • Conference paper
Knowledge-Based Intelligent Information and Engineering Systems (KES 2006)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 4253))

  • 1597 Accesses

Abstract

A novel algorithm particularly suited for determining and identifying the sparseness and the associated features within an unknown channel model is proposed.This is achieved by virtue of hierarchical filters which exhibit relatively steady state characteristics, when more than one of their constitutive sub-filters have to identify non-zero parts of the unknown channel. This way, sparseness is detected by comparing, in an on-line fashion, the performance of a hierarchical filter with that of the standard finite impulse response (FIR) filter trained by the least mean square (LMS) algorithm. In addition, the character and type of sparseness can be detected based on the architecture of the underlying hierarchical filter. The analysis is supported by simulations evaluated in a rigourous statistical framework.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

Similar content being viewed by others

References

  1. Burdic, W.S.: Underwater Systems Analysis. Prentice-Hall, Englewood Cliffs (1984)

    Google Scholar 

  2. Balakrishan, J., Sethares, W.A., Johnson Jr., C.R.: Approximate Channel Identification via δ-Signed Correlation. Int. J. Adapt. Control Signal Proc. 16, 309–323 (2002)

    Article  Google Scholar 

  3. Nascimento, V.: Analysis of the Hierarchical LMS Algorithm. IEEE SIgnal Proc. Let. 10(3), 78–81 (2003)

    Article  Google Scholar 

  4. Stoica, P., Agrawal, M., Ahgren, P.: On the Hierarchical Least-Squares Algorithm. IEEE Comunication Let. 6(4), 153–155 (2002)

    Article  Google Scholar 

  5. Macleod, M.: Performance of the Hierarchical LMS Algorithm. IEEE Comunication Let. 9(12), 436–437 (2002)

    Google Scholar 

  6. Martin, R.K., Sethares, W.A., Williamson, R.C., Johnson Jr., C.R.: Exploiting Sparsity in Adaptive Filters. IEEE Trans. Signal Proc. 50(8), 1883–1894 (2002)

    Article  Google Scholar 

  7. Woo, T.-K.: Fast Hierarchical Least Mean Square Algorithm. IEEE SIgnal Proc. Let. 8(11), 289–291 (2001)

    Article  Google Scholar 

  8. Widrow, B., Stearns, S.: Adaptive Signal Processing. Prentice-Hall, Englewood Cliffs (1985)

    MATH  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Athens Information Technology, Greece

    C. Boukis & L. C. Polymenakos

Authors
  1. C. Boukis
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. L. C. Polymenakos
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. School of Design, Engineering and Computing, Bournemouth University, UK

    Bogdan Gabrys

  2. Centre for SMART Systems, School of Environment and Technology, University of Brighton, BN2 4GJ, Brighton, UK

    Robert J. Howlett

  3. School of Electrical and Information Engineering, Knowledge Based Intelligent Engineering Systems Centre, University of South Australia, Mawson Lakes, 5095, SA, Australia

    Lakhmi C. Jain

Rights and permissions

Reprints and permissions

Copyright information

© 2006 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Boukis, C., Polymenakos, L.C. (2006). Using Hierarchical Filters to Detect Sparseness in Unknown Channels. In: Gabrys, B., Howlett, R.J., Jain, L.C. (eds) Knowledge-Based Intelligent Information and Engineering Systems. KES 2006. Lecture Notes in Computer Science(), vol 4253. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11893011_155

Download citation

  • DOI: https://doi.org/10.1007/11893011_155

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-46542-3

  • Online ISBN: 978-3-540-46544-7

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics