Grid matching for sparse signal recovery in compressive sensing

Author

Jagannath, Rakshith ; Leus, Geert ; Pribic, Radmila

Author_Institution

Fac. of EEMCS, Delft Univ. of Technol., Delft, Netherlands

fYear

2012

fDate

Oct. 31 2012-Nov. 2 2012

Firstpage

111

Lastpage

114

Abstract

Sparse signal recovery is often performed over an estimation grid whose choice affects the recovery performance. The grid mismatch effect is posed as a total least squares problem based on the errors in variables (EIV) model. An existing approach to model the mismatch namely the interpolation approach is interpreted as an EIV model. The grid mismatch is solved by an alternating descent algorithm, which alternates between basis-pursuit (BP) and least squares (LS) as well as its extension wherein BP is solved by a Bayesian algorithm. These algorithms are compared with respect to the SNR and grid offset for the Nyquist and upsampled grids.

Keywords

compressed sensing; interpolation; least squares approximations; signal reconstruction; BP; Bayesian algorithm; EIV model; Nyquist grids; alternating descent algorithm; basis-pursuit; compressive sensing; errors in variables model; grid matching estimation; grid mismatch effect; grid offset; interpolation approach; least squares problem; sparse signal recovery; upsampled grids; Compressed sensing; Direction of arrival estimation; Estimation; Interpolation; Signal processing algorithms; Signal to noise ratio; Sparse signal recovery; basis pursuit; direction of arrival estimation; errors in variables model; fast Laplace; grid mismatch; total least squares;

fLanguage

English

Publisher

ieee

Conference_Titel

Radar Conference (EuRAD), 2012 9th European

Conference_Location

Amsterdam

Print_ISBN

978-1-4673-2471-7

Type

conf

Filename

6450709