Efficient positivity test algorithms for parametric and nonparametric sequences of covariance estimates

  • Authors:
  • Issa M. S. Panahi

  • Affiliations:
  • Department of Electrical Engineering, University of Texas at Dallas, Richardon, TX

  • Venue:
  • IEEE Transactions on Signal Processing
  • Year:
  • 2009

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Abstract

In statistical signal processing problems involving second-order information of data such as covariance estimation, spectral factorization, and optimal filtering, one often needs to test positivity of a real sequence obtained from the finite length of data as covariance estimates. In this correspondence, we present efficient time-domain algorithms for testing nonnegativity of real finite nonparametric and linearly parametric sequences as valid covariance estimates. For a parametric sequence, the algorithm searches entire parameter space to find a unique set of parameters for which the sequence is positive-definite. Examples show performance of the proposed algorithms versus direct use of DFT/FFT.