On AR representations for cyclostationary processes

  • Authors:

  • P. J. Sherman;L. B. White;R. R. Bitmead

  • Affiliations:

  • Iowa State Univ., Ames, IA, USA;Dept. of Math., Maryland, Univ. College Park, MD, USA;Lab. d'Inf. Signaux & Syst., Nice Univ., France

  • Venue:
  • ICASSP '93 Proceedings of the Acoustics, Speech, and Signal Processing, 1993. ICASSP-93 Vol 4., 1993 IEEE International Conference on - Volume 04
  • Year:
  • 1993

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Abstract

The authors consider autoregressive (AR) types of wide sense cyclostationary (WSC) processes. The comparative performance of the discrete Fourier transform (DFT) autoregressive (AR) methods of estimating the time-periodic spectral density of an AR(2) WSC process is provided. Problems with these methods are addressed in the case of uncertainty of the process period. Examples concerning an AR(2) process subjected to period drift and randomness are provided to show that the time-varying spectral estimate converges to a time-invariant one. Results from stochastic differential equations which support this behaviour are cited. Finally, the method of extended Kalman filtering is proposed to track a slowly time-varying period.