Frequency Estimation of Sinusoidal Signals in Alpha-Stable Noise Using Subspace Techniques

Abstract

In the frequency estimation of sinusoidal signals observed in impulsive noise environments, techniques based on Gaussian noise assumption are unsuccessful. One possible way to find better estimates is to model the noise as an alpha-stable process and to use the fractional lower order statistics of data to estimate the signal parameters. In this work noise and signal subspace methods, namely MUSIC and Principal Component-Bartlett, are applied to fractional lower order statistics of sinusoids embedded in alpha-stable noise. The simulation results show that techniques based on lower order statistics are superior to their second order statistics-based counterparts, especially when the noise exhibits a strong impulsive attitude. 1. Introduction Most of the work on the frequency estimation problem assumes that the additive noise has Gaussian distribution. This is partly because of the nice properties of the Gaussian model which allows for simplification of the theoretical work and decreases..