TY - JOUR
T1 - Optimal kernels for Wigner-Ville spectral estimation
AU - Sayeed, Akbar M.
AU - Jones, Douglas L.
N1 - Funding Information:
This work was supported by the National Science Foundat.ion under Grant No. MIP 90-12747, the Joint Services Electronics Program under Grant No. N00014-90-J-1270, and the Schlum-berger Foundation.
Publisher Copyright:
© 1994 IEEE
PY - 1994
Y1 - 1994
N2 - 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.
AB - 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.
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U2 - 10.1109/ICASSP.1994.389817
DO - 10.1109/ICASSP.1994.389817
M3 - Conference article
AN - SCOPUS:85031573504
VL - 4
SP - IV297-IV300
JO - Proceedings - ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing
JF - Proceedings - ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing
SN - 0736-7791
M1 - 389817
T2 - Proceedings of the 1994 IEEE International Conference on Acoustics, Speech and Signal Processing. Part 2 (of 6)
Y2 - 19 April 1994 through 22 April 1994
ER -