Direction-finding based on the theory of super-resolution in sparse recovery algorithms

Cheng Yu Hung, Mostafa Kaveh

Research output: Chapter in Book/Report/Conference proceedingConference contribution

3 Scopus citations

Abstract

The problem of recovering directions-of-arrival in the sparse signal model with multiple snapshots is considered. Based on the theory of super resolution, multiple snapshots are used to jointly estimate directions-of-arrival in the continuous domain. Instead of uniformly discretizing the search range, interpolation preprocessing on the estimated super-resolution directions is suggested leading to a sparse convex optimization formulation. Moreover, a first order iterative algorithm is employed to reduce the computational time. A good selection of regularization parameter is guaranteed via the modified generalized cross validation (GCV). Numerical results demonstrate the performance of the proposed methods.

Original languageEnglish (US)
Title of host publication2015 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2015 - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages2404-2408
Number of pages5
ISBN (Electronic)9781467369978
DOIs
StatePublished - Aug 4 2015
Event40th IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2015 - Brisbane, Australia
Duration: Apr 19 2014Apr 24 2014

Publication series

NameICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
Volume2015-August
ISSN (Print)1520-6149

Other

Other40th IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2015
CountryAustralia
CityBrisbane
Period4/19/144/24/14

Keywords

  • Directions of Arrival
  • Generalized Cross Validation
  • Multiple Measurement Vectors
  • Sparsity
  • Super Resolution

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