Separation of Dependent Autoregressive Sources Using Joint Matrix Diagonalization

This letter proposes a novel technique for the blind separation of autoregressive (AR) sources. The latter relies on the joint diagonalization (JD) of appropriate AR matrix coefficients of the observed signals and can be applied to the separation of statistically dependent sources. The developed algorithm is referred to as `DARSS-JD´ (for Dependent AR Source Separation using JD). Through the simulation experiments, DARSS-JD is shown to overcome existing second order separation methods with a relatively moderate computational cost.