Factor analysis is a popular tool in multivariate statistics, applied in several areas of study such as psychology, economics, chemistry and signal processing. Given a set of observed random variables, factor analysis aims at explaining and analyzing the correlation between these random variables. This is done by finding a meaningful structural model representation for the correlation matrix of the observed random variables, and subsequently estimating the underlying model parameters. In this paper, we focus on factor analysis methods applied to a commonly used signal model for sensor arrays applications and use it to jointly estimate the underlying model parameters. In addition we discuss practical considerations of these methods.