Subspace-based direction finding in alpha-stable noise environments

There exist real world applications in which impulsive channels tend to produce large amplitude interferences more frequently than Gaussian channels. The stable law has been shown to successfully model noise over certain impulsive channels. The authors propose subspace-based methods for the direction-of-arrival estimation problem in impulsive noise environments. They define the covariation matrix of the array sensor outputs and show that eigendecomposition-based methods, such as the MUSIC algorithm, can be applied to the sample covariation matrix to extract the bearing information from the measurements