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      https://www.ias.ac.in/article/fulltext/sadh/041/11/1275-1287

    • Keywords

       

      Compressed sensing; fusion; sparse signal reconstruction; multiple measurement vectors.

    • Abstract

       

      We consider the recovery of sparse signals that share a common support from multiple measurement vectors. The performance of several algorithms developed for this task depends on parameters like dimension of the sparse signal, dimension of measurement vector, sparsity level, measurement noise. We propose a fusion framework, where several multiple measurement vector reconstruction algorithms participate and the final signal estimate is obtained by combining the signal estimates of the participating algorithms. We present the conditions for achieving a better reconstruction performance than the participating algorithms. Numerical simulations demonstrate that the proposed fusion algorithm often performs better than the participating algorithms.

    • Author Affiliations

       

      K G DEEPA1 SOORAJ K AMBAT1 2 K V S HARI1

      1. Statistical Signal Processing Laboratory, Department of Electrical Communication Engineering, Indian Institute of Science, Bangalore 560012, India
      2. Naval Physical and Oceanographic Laboratory, Defence Research and Development Organisation, Kochi 682021, India
    • Dates

       
  • Sadhana | News

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