Identifiability of the Parafac Model for Polarized Source Mixture on a Vector Sensor Array
Xijing Guo, Sébastian Miron, David Brie · 2008
By means of the parallel factor (PARAFAC) decomposition, we present a novel method working on a vector-sensor array for blind separation of polarized sources in virtue of their distinct spatial and temporal signatures. Identifiability is studied, and explicit constraints on the sources are derived to ensure the data model identifiable. We show, by numerical simulations, that the estimation performance can approach that of non-blind estimation by optimally designing the source polarizations.