Blind source recovery: algorithms for static and dynamic environments

This paper integrates our contributions in the domain of blind source separation and blind source deconvolution, both in static and dynamic environments. We focus on the use of the state space formulation and the development of a generalized optimization framework, using Kullback-Liebler divergence as the performance measure subject to the constraints of a state space representation. Various special cases are subsequently derived from this general case and are compared with material in recent literature. Some of these reported works have also been implemented in dedicated hardware/software and experimental designs have been compared with their computer simulations.