Direction of arrival estimation in the presence of distributed noise sources: Cumulant based approach

Abstract

In the past few years there have been attempts to develop subspace methods for DoA (direction of arrival) estimation using a fourth-order cumulant which is known to de-emphasize Gaussian background noise. To gauge the relative performance of the cumulant MUSIC (MUltiple SIgnal Classification) (c-MUSIC) and the standard MUSIC, based on the covariance function, an extensive numerical study has been carried out, where a narrow-band signal source has been considered and Gaussian noise sources, which produce a spatially correlated background noise, have been distributed. These simulations indicate that, even though the cumulant approach is capable of de-emphasizing the Gaussian noise, both bias and variance of the DoA estimates are higher than those for MUSIC. To achieve comparable results the cumulant approach requires much larger data, three to ten times that for MUSIC, depending upon the number of sources and how close they are. This is attributed to the fact that in the estimation of the cumulant, an average of a product of four random variables is needed to make an evaluation. Therefore, compared to those in the evaluation of the covariance function, there are more cross terms which do not go to zero unless the data length is very large. It is felt that these cross terms contribute to the large bias and variance observed in c-MUSIC. However, the ability to de-emphasize Gaussian noise, white or colored, is of great significance since the standard MUSIC fails when there is colored background noise. Through simulation it is shown that c-MUSIC does yield good results, but only at the cost of more data.