Skip to main content
SpringerLink
Log in

A Prewhitening RLS Projection Alternated Subspace Tracking (PAST) Algorithm

  • Conference paper
Advances in Neural Networks - ISNN 2006 (ISNN 2006)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 3971))

Included in the following conference series:

  • 97 Accesses

Abstract

In this paper we propose a new principal component extracting algorithm based on the PAST. A prewhitening procedure is introduced, which makes it numerically robust. The estimation capability of the proposed algorithm is demonstrated by computer simulations of DOA (Degree of Arrival) estimation. The estimation results of the proposed PAST outperform those of the ordinary PAST.

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

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 84.99
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 109.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Oja, E.: A Simplified Neuron Model as a Principal Component Analyzer. J. Math. Biol. 15, 267–273 (1982)

    Article  MATH  MathSciNet  Google Scholar 

  2. Diamantaras, K.I., Kung, S.Y. (eds.): Principal Component Neural Networks—Theory and Applications. Wiley, New York (1996)

    MATH  Google Scholar 

  3. Yang, B.: Projection Approximation Subspace Tracking. IEEE Trans. Signal Proc. 43, 95–107 (1995)

    Article  MATH  Google Scholar 

  4. Haykin, S.: Adaptive Filter Theory, 3rd edn. Prentice-Hall, Upper Saddle River (1996)

    Google Scholar 

  5. Douglas, S.C.: Numerically - Robust. O(N2) Recursive Least-Squares Estimation Using Least Squares Prewhitening. In: Proceeding of International Conference of Acoustics, Speech, and Signal Processing, vol. 1, pp. 412–415 (2000)

    Google Scholar 

  6. Dasilva, F.M., Almeida, L.B.: A Distributed Decorrelation Algorithm. In: Gelenbe, E. (ed.) Neural Networks: Advances and Applications, pp. 145–163. Elsevier Science, Amsterdam (1991)

    Google Scholar 

  7. Douglas, S.C., Cichocki, A.: Neural Networks for Blind Decorrelation of Signals. IEEE Trans. Signal Processing 45, 2829–2842 (1997)

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Dept. of Electronics Eng., Sejong University, Kunja, Kwangjin, 98, 143-747, Seoul, Korea

    Junseok Lim

  2. S/W Lab. R&D Group 2 Mobile Communication Division Telecommunication Network Business, SAMSUNG Electronics Co.,LTD., Dong Suwon, P.O. Box 105, 442-600, Korea

    Joonil Song

  3. Gangwon Provincial Univ., Gyowhangli Jumoonjin Gangneung, GangwonDo, 210-804, Korea

    Yonggook Pyeon

Authors
  1. Junseok Lim
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Joonil Song
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Yonggook Pyeon
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. Department of Mechanical and Automation Engineering, The Chinese University of Hong Kong, Shatin, New Territories, Hong Kong, China

    Jun Wang

  2. Computational Intelligence Laboratory, School of Computer Science and Engineering, University of Electronic Science and Technology of China, 610054, Chengdu, P.R. China

    Zhang Yi

  3. Department of Electrical Engineering, University of Louisville, 40292, Louisville, KY, U.S.A

    Jacek M. Zurada

  4. Laboratory for Computational Biology, Shanghai Center for Systems Biomedicine, 800 Dong Chuan Rd., 200240, Shanghai, China

    Bao-Liang Lu

  5. School of Electrical and Electronic Engineering, University of Manchester, UK

    Hujun Yin

Rights and permissions

Reprints and permissions

Copyright information

© 2006 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Lim, J., Song, J., Pyeon, Y. (2006). A Prewhitening RLS Projection Alternated Subspace Tracking (PAST) Algorithm. In: Wang, J., Yi, Z., Zurada, J.M., Lu, BL., Yin, H. (eds) Advances in Neural Networks - ISNN 2006. ISNN 2006. Lecture Notes in Computer Science, vol 3971. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11759966_199

Download citation

  • DOI: https://doi.org/10.1007/11759966_199

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-34439-1

  • Online ISBN: 978-3-540-34440-7

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation