Ricerc@Sapienza

Factor analysis is a well-known model for describing the covariance structure among a set of manifest variables through a limited number of unobserved factors. When the observed variables are collected at various occasions on the same statistical units, the data have a three-way structure and standard factor analysis may fail to discover the interrelations among the variables. To overcome these limitations, three-way models can be adopted. Among them, the so-called Parallel Factor (Parafac) model can be applied. In this article, the structural version of such a model, i.e.

Factor analysis is a well-known method for describing the covariance structure among a set of manifest variables through a limited number of unobserved factors. When the observed variables are collected at various occasions on the same statistical units, the data have a three-way structure and standard factor analysis may fail. To overcome these limitations, three-way models, such as the Parafac model, can be adopted. It is often seen as an extension of principal component analysis able to discover unique latent components.

This paper addresses performance characterization of a direction of arrival (DoA) estimator in the low signal-to-noise-ratio (SNR) region. The case of a sensor array simultaneously collecting signals emitted at multiple carrier frequencies by a single source is considered. A maximum likelihood (ML) approach is used as a reference method for DoA estimation and its accuracy is characterized in terms of mean square error (MSE). It is well known that, for SNR values included in the so-called threshold region, the DoA estimation accuracy decreases rapidly, due to the presence of outliers.