Improving resolution for autoregressive spectral estimation by decimation

In this paper we present a method for efficiently improving the resolution of autoregressive spectral estimation algorithms. We derive the exact autoregressive spectrum for K complex sinusoids in additive white noise. From this equation resolution boundaries are constructed which give the resolution in terms of the model order and the signal-to-noise ratio. Simulation results are used to compare the resolution boundaries for decimated and undecimated spectra. Our results demonstrate that decimation by D with a model order M yields the same resolution as a model order MD used with the undecimated signal, and that decimation reduces the computation.