• DocumentCode
    1433508
  • Title

    A statistical resolution theory of the AR method of spectral analysis

  • Author

    Zhang, Q.T.

  • Author_Institution
    Dept. of Electr. Eng., Toronto Univ., Ont., Canada
  • Volume
    46
  • Issue
    10
  • fYear
    1998
  • fDate
    10/1/1998 12:00:00 AM
  • Firstpage
    2757
  • Lastpage
    2766
  • Abstract
    The autoregressive (AR) method of spectral analysis is widely used in diverse areas for its solid theoretical foundation, interesting physical interpretation, computational efficiency, and, more importantly, high resolution capability. Various aspects of its statistical performance have been investigated. However, the resolution probability that provides the most rigorous description of the spectrum resolution capability is still not available in the literature. In this paper, by formulating the resolution event in the framework of statistical decision theory and directly determining its probability from its characteristic function, we obtain an exact asymptotic formula for the probability of resolution. On this basis, we determine the limiting resolving behavior of the sample AR spectrum and develop the corresponding geometrical insight in the parametric space. Simulation and numerical results are also presented to confirm and illustrate the theory
  • Keywords
    autoregressive processes; decision theory; parameter estimation; probability; signal resolution; signal sampling; spectral analysis; statistical analysis; AR method; autoregressive method; characteristic function; computational efficiency; exact asymptotic formula; high resolution; limiting resolving behavior; narrowband signals; parametric space; resolution probability; sample AR spectrum; signal processing; simulation results; spectral analysis; spectrum resolution; statistical decision theory; statistical performance; statistical resolution theory; Computational efficiency; Decision theory; Entropy; Filters; Frequency; Limiting; Probability; Solids; Spectral analysis; White noise;
  • fLanguage
    English
  • Journal_Title
    Signal Processing, IEEE Transactions on
  • Publisher
    ieee
  • ISSN
    1053-587X
  • Type

    jour

  • DOI
    10.1109/78.720377
  • Filename
    720377