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Publication - Dr Vladislav Tadic

    Better than ℓ0 recovery via blind identification

    Citation

    Porter, RJ, Tadic, V & Achim, A, 2016, ‘Better than ℓ0 recovery via blind identification’. in: 2015 IEEE Global Conference on Signal and Information Processing, GlobalSIP 2015: Proceedings of a meeting held 14-16 December 2015, in Orlando, FL, USA. Institute of Electrical and Electronics Engineers (IEEE), pp. 1280-1284

    Abstract

    In this work, we propose a novel approach to multiple measurement vector (MMV) compressed sensing. We show that by exploiting the statistical properties of the sources, we can do better than previously derived lower bounds in this context. We show that in the MMV case, we can identify the active sources with fewer sensors than sources. We first develop a general framework for recovering the sparsity profile of the sources by combining ideas from compressed sensing with blind identification methods. We do this by comparing the large known sensing matrix to the smaller matrix estimated by a blind identification method. Finally, we demonstrate the performance of this technique with a variety of data and blind identification methods, and show that under certain assumptions, it is possible to identify the active sources with only 2 sensors, regardless of the number of sources.

    Full details in the University publications repository