Double Blind Separation of Compressively Sensed Signals

This work focuses on the problem of jointly separating and reconstructing source signals from compressively sensed mixtures. The proposed approach combines the concept of blind compressive sensing (BCS) with the model of blind source separation (BSS). Specifically, the unknown source signals are assumed to admit some sparse representations with respect to an unknown dictionary. We propose an appropriate cost function for the problem together with an alternating minimization scheme for finding a solution. A fixed-point analysis as well as experiments on synthetic and on real data will be provided to demonstrate the efficiency and the convergence behavior of the proposed algorithm.

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