Abstract
Blind Source Recovery (BSR) denotes recovery of original sources/signals from environments that may include convolution, temporal variation, and even nonlinearity. It also infers the recovery of sources even in the absence of precise environment identifiability. This paper describes, in a comprehensive fashion, a generalized BSR formulation achieved by the application of stochastic optimization principles to the Kullback-Liebler divergence as a performance functional subject to the constraints of the general (i.e., nonlinear and time-yawing) state space representation. This technique is used to derive update laws for nonlinear time-varying dynamical systems, which are subsequently specialized to time-invariant and linear systems. Further, the state space demixing network structures have been exploited to develop learning rules, capable of handling most filtering paradigms, which can be conveniently extended to nonlinear models. In the special cases, distinct linear state-space algorithms are presented for the minimum phase and non-minimum phase mixing environment models. Conventional (FIR/ER) filtering models are subsequently derived from this general structure and are compared with material in the recent literature. Illustrative simulation examples are presented to demonstrate the online adaptation capabilities of the developed algorithms. Some of this reported work has also been implemented in dedicated hardware/software platforms.