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A Brief Overview on Least Square Spectral Analysis and Power Spectrum

  • Ingudam Gomita
  • Sandeep Chauhan
  • Raj Kumar Sagar
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 437)

Abstract

LSSA, i.e., least square spectral analysis method can be used to calculate the power density function of a fully populated covariance matrix. It fulfills the limitation cause by the fast Fourier transform (FFT), i.e., equally spaced and equally weighted time series. This time of experimental time series used LSSA (which used projection theorem) so that it can also analyze the time series, which is unequally spaced and unequally weighted. Signal-to noise ratio (SNR) is used to find out the probability density function.

Keywords

LSSA Power density function Signal-to-noise ratio Projection theorem 

References

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    Vanicek, P.: Further development and properties of the spectral analysis by least-squares. Astrophys. Space Sci. 12, 10–33 (1971)CrossRefGoogle Scholar
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    Pagiatakis, S.D.: Application of the least-squares spectral analysis to superconducting gravimeter data treatment and analysis. Cahiers du Centre Europeen de Geodynamique et Seismologie 17, 103–113 (2000)Google Scholar
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    Wells, D., Vanicek, P., Pagiatakis, S.: least square spectral analysis revisited (1985)Google Scholar

Copyright information

© Springer Science+Business Media Singapore 2016

Authors and Affiliations

  • Ingudam Gomita
    • 1
  • Sandeep Chauhan
    • 1
  • Raj Kumar Sagar
    • 1
  1. 1.Amity UniversityNoidaIndia

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