Time-frequency decomposition of multivariate multicomponent signals

- Decomposition of multicomponent multivariate signals which partially overlap in the joint time-frequency domain is presented.
- The method is based on the eigenvectors of the signal autocorrelation matrix.
- The multivariate signal components are obtained as linear combinations of the eigenvectors that minimize the concentration measure in the time-frequency domain.
- Simulation results validate the proposed method.

A solution of the notoriously difficult problem of characterization and decomposition of multicomponent multivariate signals which partially overlap in the joint time-frequency domain is presented. This is achieved based on the eigenvectors of the signal autocorrelation matrix. The analysis shows that the multivariate signal components can be obtained as linear combinations of the eigenvectors that minimize the concentration measure in the time-frequency domain. A gradient-based iterative algorithm is used in the minimization process and for rigor, a particular emphasis is given to dealing with local minima associated with the gradient descent approach. Simulation results over illustrative case studies validate the proposed algorithm in the decomposition of multicomponent multivariate signals which overlap in the time-frequency domain.