A novel aliasing-free subband information fusion approach for wideband sparse spectral estimation

A novel aliasing-free subband information fusion approach for wideband sparse spectral estimation Wideband sparse spectral estimation is generally formulated as a multi-dictionary/multi-measurement (MD/MM) problem which can be solved by using group sparsity techniques. In this paper, the MD/MM problem is reformulated as a single sparse indicative vector (SIV) recovery problem at the cost of introducing an additional system error. Thus, the number of unknowns is reduced greatly. We show that the system error can be neglected under certain conditions. We then present a new subband information fusion (SIF) method to estimate the SIV by jointly utilizing all the frequency bins. With orthogonal matching pursuit (OMP) leveraging the binary property of SIV’s components, we develop a SIF-OMP algorithm to reconstruct the SIV. The numerical simulations demonstrate the performance of the proposed method. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png EURASIP Journal on Advances in Signal Processing Springer Journals

A novel aliasing-free subband information fusion approach for wideband sparse spectral estimation

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Publisher
Springer International Publishing
Copyright
Copyright © 2017 by The Author(s)
Subject
Engineering; Signal,Image and Speech Processing; Quantum Information Technology, Spintronics
eISSN
1687-6180
D.O.I.
10.1186/s13634-017-0494-8
Publisher site
See Article on Publisher Site

Abstract

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

Journal

EURASIP Journal on Advances in Signal ProcessingSpringer Journals

Published: Aug 23, 2017

References

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