EVOLUTIONARY PERIODOGRAM FOR NONSTATIONARY SIGNALS

In this paper, we present a novel estimator for the time-dependent spe ctrum of a nonstationary signal. By modeling the signal, at any given frequency, as having a time-varying amplitude accurately represented h y an orthonormal basis expansion, we are able to compute a minimum mea n-squared error estimate of this time-varying amplitude. Repeating the process over all frequencies, we obtain a power distribution as a fun ction of time and frequency that is consistent with the Wold-Cramer ev olutionary spectrum. Based on the model assumptions, we develop the ev olutionary periodogram (EP) for nonstationary signals, an estimator an alogous to the periodogram used in the stationary case. We also derive the time-frequency resolution of the new estimator. Our approach is f ree of some of the drawbacks of the bilinear distributions and of the short-time Fourier transform spectral estimates. It is guaranteed to p roduce nonnegative spectra without the cross-term behavior of the bili near distributions, and it does not require windowing of data in the t ime domain. Examples illustrating the new estimator are given.