Wideband sparse spectral estimation is generally formulated as a multi-dictionary/multi-measurement (MD/MM)\nproblem which can be solved by using group sparsity techniques. In this paper, the MD/MM problem is reformulated\nas a single sparse indicative vector (SIV) recovery problem at the cost of introducing an additional system error. Thus,\nthe number of unknowns is reduced greatly. We show that the system error can be neglected under certain\nconditions. We then present a new subband information fusion (SIF) method to estimate the SIV by jointly utilizing all\nthe frequency bins. With orthogonal matching pursuit (OMP) leveraging the binary property of SIV�s components, we\ndevelop a SIF-OMP algorithm to reconstruct the SIV. The numerical simulations demonstrate the performance of the\nproposed method.
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