Capon's time-frequency representation with nonstationary ar autocorrelation

In this paper, a novel approach for spectral analysis of nonstationary signals is presented. For this purpose, the Capon's Time Frequency Representation (CTFR) is employed. It is shown that the CTFR is an upper bound on the range of nonunique solutions for power estimation of a complex sinusoid contaminated with unknown noise. A new local autocorrelation function using a Nonstationary Auto-Regressive (NAR) model is defined and used in the CTFR. This method efficiently models the autocorrelations of NAR processes. Synthetic signals are generated in order to illustrate the superiority of the CTFR with NAR model in comparison to other methods.