The state space framework for blind dynamic signal extraction and recovery

The paper describes a framework in the form of an optimization of a performance index subject to the constraints of a dynamic network, represented in the state space. The performance index is a measure of statistical dependence among the outputs of the network, namely, the relative entropy also known as the Kullback-Leibler divergence. The network is represented as (either discrete or continuous time) state space dynamics. Update laws are derived in the general cases. Moreover, in the discrete-time case, they are shown to specialize to the FIR and IIR network representations.