Skip to main content
SpringerLink
Log in

Introduction to Adaptive Signal and Array Processing

  • Chapter
  • First Online:
Fundamentals of Adaptive Signal Processing

Part of the book series: Signals and Communication Technology ((SCT))

Abstract

In the study of signal processing techniques, the term adaptive is used when a system (analogue or digital) is able to adjust their own parameters in response to external stimulations. In other words, an adaptive system autonomously changes its internal parameters for achieving a certain processing goal such as, for example, the minimization of the effect of noise overlying the signal of interest (SOI).

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

Access this chapter

Log in via an institution
eBook
USD 16.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Hardcover Book
USD 119.99
Price excludes VAT (USA)
  • Durable hardcover 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

Notes

  1. 1.

    Definition of ISI from USA Federal Standard 1037C, titled Glossary of Telecommunication Terms: in a digital transmission system, distortion of the received signal, which distortion is manifested in the temporal spreading and consequent overlap of individual pulses to the degree that the receiver cannot reliably distinguish between changes of state, i.e., between individual signal elements.

References

  1. Kay SM (1993) Fundamental of statistical signal processing estimation theory. Prentice Hall, Englewood Cliffs, NJ

    Google Scholar 

  2. Kuo SM, Morgan DR (1999) Active noise control: a tutorial review. Proc IEEE 87(6):943–973

    Article  Google Scholar 

  3. Van Trees HL (2002) Optimum array processing, Part IV: detection, estimation and modulation theory. Wiley, New York

    Book  Google Scholar 

  4. R Lippmann (1987) An introduction to computing with neural nets. IEEE ASSP Magazine, vol 4, no 2, Aprile 1987

    Google Scholar 

  5. Rumelhart DE, McClelland JL, The PDP Research Group (1986) Parallel distributed processing: explorations in the microstructure of cognition, vol 1: foundations. MIT Press, Cambridge, MA

    Google Scholar 

  6. B Widrow, M Lehr (1990) 30 years of adaptive neural networks: perceptron, adaline and backpropagation. Proc IEEE 78(9), Settembre 1990

    Google Scholar 

  7. JJ Hopfield, DW Tank (1986) Computing with neural circuits: a model. Science 233: 625–633, August 1986

    Google Scholar 

  8. T Kohonen (2001) Self-organizing maps, 3rd extended edn. Springer series in information sciences, vol 30. Springer, Berlin

    Google Scholar 

  9. J Principe, A Chicocki, L Xu, E Oja, D Erdogmus (Guest eds) (2004) Special issue on—Information theoretic learning. IEEE Trans Neural Netw 15(4): 789–791, July 2004

    Google Scholar 

  10. Widrow B, Stearns SD (1985) Adaptive signal processing. Prentice Hall, Englewood Cliffs, NJ

    MATH  Google Scholar 

  11. Haykin S (1996) Adaptive filter theory, 3rd edn. Prentice Hall, Upper Saddle River, NJ

    Google Scholar 

  12. Wiener N (1949) Extrapolation, interpolation and smoothing of stationary time series, with engineering applications. Wiley, New York

    MATH  Google Scholar 

  13. Kailath T (1974) A view of three decades of linear filtering theory. IEEE Trans Inf Theory IT20(2):146–181

    Article  MathSciNet  Google Scholar 

  14. Box GEP, Jenkins GM (1970) Time series analysis: forecasting and control. Holden-Day, San Francisco

    MATH  Google Scholar 

  15. Bode HW, Shannon CE (1950) A simplified derivation of linear least squares smoothing and prediction theory. Proc IRE 38:417–425

    Article  MathSciNet  Google Scholar 

  16. Orfanidis SJ (1996) Introduction to signal processing. Prentice Hall, Upper Saddle River, NJ

    Google Scholar 

  17. Manolakis DG, Ingle VK, Kogon SM (2000) Statistical and adaptive signal processing. McGraw-Hill, New York

    Google Scholar 

  18. Burg JP (1968) A new analysis technique for time data series. NATO Advanced Study Institute on Signal Processing, Enschede, Netherlands

    Google Scholar 

  19. JA Cadzow (1990) Signal processing via least squares error modeling. IEEE ASSP Magazine, pp 12–31,October 1990

    Google Scholar 

  20. Benesty J, Gänsler T, Morgan DR, Sondhi MM, Gay SL (2001) Advances in network and acoustic echo cancellation. Springer, Berlin. ISBN 978-3-540-41721-7

    Book  MATH  Google Scholar 

  21. Hänsler E, Schmidt G (eds) (2006) Topics in acoustic echo and noise control. Spinger, Berlin. ISBN 978-3-540-33212-1

    Google Scholar 

  22. E Oja, S Harmeling, L Almeida (Guest eds) (2004) Special issue on—Independent component analysis and beyond. Signal Process 84(2), February 2004

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. DIET, Sapienza University of Rome, Rome, Italy

    Aurelio Uncini

Authors
  1. Aurelio Uncini
    View author publications

    You can also search for this author in PubMed Google Scholar

Rights and permissions

Reprints and permissions

Copyright information

© 2015 Springer International Publishing Switzerland

About this chapter

Cite this chapter

Uncini, A. (2015). Introduction to Adaptive Signal and Array Processing. In: Fundamentals of Adaptive Signal Processing. Signals and Communication Technology. Springer, Cham. https://doi.org/10.1007/978-3-319-02807-1_2

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-02807-1_2

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-02806-4

  • Online ISBN: 978-3-319-02807-1

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics

Search

Navigation