Data-adaptive regularization for DOA estimation using sparse spectrum fitting

J. Zheng, M. Kaveh

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

1 Scopus citations

Abstract

Regularization parameter selection is critical to the performance of many sparsity-exploiting Direction-Of-Arrival (DOA) estimation algorithms. In this paper, we propose an automatic selector for choosing this parameter in the DOA estimation algorithm presented in [1], which is based on the analysis of its optimality conditions. This selector requires very limited prior information and is computationally efficient. Through simulation examples, the effectiveness and robustness of the selector are illustrated.

Original languageEnglish (US)
Title of host publication2013 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2013 - Proceedings
Pages3957-3961
Number of pages5
DOIs
StatePublished - Oct 18 2013
Event2013 38th IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2013 - Vancouver, BC, Canada
Duration: May 26 2013May 31 2013

Publication series

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

Other

Other2013 38th IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2013
CountryCanada
CityVancouver, BC
Period5/26/135/31/13

Keywords

  • Direction-Of-Arrival
  • Regularization Parameter Selection
  • Sparse Representation

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