  With their ability to handle an increased amount of information, multivariate
and multichannel signals can be used to solve problems normally not solvable
with signals obtained from a single source. One such problem is the
decomposition signals with several components whose domains of support
significantly overlap in both the time and the frequency domain, including the
joint time-frequency domain. Initially, we proposed a solution to this problem
based on the Wigner distribution of multivariate signals, which requires the
attenuation of the cross-terms. In this paper, an advanced solution based on an
eigenvalue analysis of the multivariate signal autocorrelation matrix, followed
by their time-frequency concentration measure minimization, is presented. This
analysis provides less restrictive conditions for the signal decomposition than
in the case of Wigner distribution. The algorithm for the components separation
is based on the concentration measures of the eigenvector time-frequency
representation, that are linear combinations of the overlapping signal
components. With an increased number of sensors/channels, the robustness of the
decomposition process to additive noise is also achieved. The theory is
supported by numerical examples. The required channel dissimilarity is
statistically investigated as well.
