Performance analysis of multitaper spectrum estimation

We characterize the Multitaper Spectral Estimation method as a tool for stationary signal analysis. We compare its performance to the conventional periodogram, the parametric autoregressive and multitaper autoregressive spectral estimates. We analyze single and two frequency sinusoids with additive Gaussian white noise and autoregressive processes of orders 2, 4 and 24. We extend its application to non-stationary signals and develop the multitaper spectrogram. We test the spectrograms with simulated non-stationary autoregressive process of order 2 as the magnitude of its poles vary between 0 and 1 and the angle of the poles vary between 0 and pi. Our results show that the multitaper spectral estimate can be parameterized and is more accurate than others tested for non-sinusoidal signals. We also show applications to aero-acoustic data analysis.