Spectrum estimation by noise-compensated data extrapolation
High-resolution spectrum estimation techniques have been extensively studied in recent publications. Knowledge of the noise variance is vital for spectrum estimation from noise-corrupted observations. This paper presents the use of noise compensation and data extrapolation for spectrum estimation. We assume that the observed data sequence can be represented by a set of autoregressive parameters. A recently proposed iterative algorithm is then used for noise variance estimation while autoregressive parameters are used for data extrapolation. We also present analytical results to show the exponential decay characteristics of the extrapolated samples and the frequency domain smoothing effect of data extrapolation. Some statistical results are also derived. The proposed noise-compensated data extrapolation approach is applied to both the autoregressive and FFT-based spectrum estimation methods. Finally, simulation results show the superiority of the method in terms of bias reduction and resolution improvement for sinusoids buried in noise.
spectrum estimation noise variance yule-walker equations autoregressive process