Abstract
We study different solutions of the so-called blind source recovery (BSR) problem: Given an m-dimensional mixture random process x(t) and an m£n mixing matrix A, solve the underdetermined equation x(t) = As(t)+e(t) with the unknown n-dimensional source process s(t) and noise e(t). For simplicity, we will assume that the processes are i.i.d. Moreover, s(t) has to satisfy additional assumptions ...
Abstract
We study different solutions of the so-called blind source recovery (BSR) problem: Given an m-dimensional mixture random process x(t) and an m£n mixing matrix A, solve the underdetermined equation x(t) = As(t)+e(t) with the unknown n-dimensional source process s(t) and noise e(t). For simplicity, we will assume that the processes are i.i.d. Moreover, s(t) has to satisfy additional assumptions such as independence or sparsity, and depending on these properties the above equation has a unique solution or reduced indeterminacies.We recall two BSR algorithms based on one of these two criteria. After discussing their properties, we show that they can be fused together in order to recover sources from a combined model. Simulations on artificial and real-world data shows the feasibility of the proposed algorithm.
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