Optimal kernels for Wigner-Ville spectral estimation

Akbar M. Sayeed, Douglas L. Jones

Research output: Contribution to journalConference articlepeer-review

Abstract

Current theories of a time-varying spectrum of a nonstationary process all involve, either by definition or by implementation, an assumption that the signal statistics vary slowly over time. This restrictive quasi-stationarity assumption limits the use of existing estimation techniques to a small class of nonstationary processes. We overcome this limitation by deriving a statistically optimal kernel, within Cohen's class of time-frequency representations, for estimating the Wigner-Ville Spectrum of a nonstationary process. Both time-frequency invariant and time-frequency varying kernels are derived. It is shown that, in the presence of any noise, optimal performance requires a nontrivial kernel, and that optimal estimation may require smoothing filters very different from those based on a quasi-stationarity assumption. An example illustrates that the optimal estimators can potentially yield tremendous improvements in performance over existing methods.

Original languageEnglish (US)
Article number389817
Pages (from-to)IV297-IV300
JournalICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
Volume4
DOIs
StatePublished - 1994
EventProceedings of the 1994 IEEE International Conference on Acoustics, Speech and Signal Processing. Part 2 (of 6) - Adelaide, Aust
Duration: Apr 19 1994Apr 22 1994

ASJC Scopus subject areas

  • Software
  • Signal Processing
  • Electrical and Electronic Engineering

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