Harmonic decomposition methods in cumulant domains (array processing)

Abstract

The authors address the bearing estimation problem of sources from array data (snapshots) in the presence of Gaussian color (spatially correlated) noises of unknown autocorrelation matrix. They demonstrate that the harmonic decomposition methods (signal and noise subspace) can easily be reformulated using fourth-order cumulant matrices instead of autocorrelations. Simulation results are presented and comparisons are made to show that the performance of the fourth-order cumulant-based methods (beamforming, MUSIC) is superior to that of their equivalent autocorrelation-based methods when the additive noise sources are colored Gaussian with unknown correlation matrix.