Jointly sparse vector recovery via reweighted ℓ1 minimization

Author

Wei, Mu-Hsin ; Scott, Waymond R., Jr. ; McClellan, James H.

Author_Institution

Sch. of Electr. & Comput. Eng., Georgia Inst. of Technol., Atlanta, GA, USA

fYear

2012

fDate

25-30 March 2012

Firstpage

3929

Lastpage

3932

Abstract

An iterative reweighted algorithm is proposed for the recovery of jointly sparse vectors from multiple-measurement vectors (MMV). The proposed MMV algorithm is an extension of the iterative reweighted ℓ₁ algorithm for single measurement problems. The proposed algorithm (M-IRL1) is demonstrated to outperform non-reweighted MMV algorithms under noiseless measurements. A regularization of the M-IRL1 algorithm is also proposed to accommodate noise. The ability to robustly handle noise is demonstrated through an electromagnetic induction application.

Keywords

electromagnetic induction; iterative methods; minimisation; M-IRL1; electromagnetic induction application; iterative reweighted ℓ₁ algorithm; iterative reweighted algorithm; jointly sparse vector recovery; multiple-measurement vectors; noiseless measurements; nonreweighted MMV algorithms; single measurement problems; Electromagnetic interference; Minimization; Noise; Noise measurement; Robustness; Signal processing algorithms; Vectors; Jointly sparse; basis pursuit; iterative reweighting; multiple-measurement vector;

fLanguage

English

Publisher

ieee

Conference_Titel

Acoustics, Speech and Signal Processing (ICASSP), 2012 IEEE International Conference on

Conference_Location

Kyoto

ISSN

1520-6149

Print_ISBN

978-1-4673-0045-2

Electronic_ISBN

1520-6149

Type

conf

DOI

10.1109/ICASSP.2012.6288777

Filename

6288777