Compressed sensing using generalized polygon samplers

Author

Gao, Kanke ; Batalama, Stella N. ; Pados, Dimitris A. ; Suter, Bruce W.

Author_Institution

Dept. of Electr. Eng., State Univ. of New York at Buffalo, Buffalo, NY, USA

fYear

2010

fDate

7-10 Nov. 2010

Firstpage

359

Lastpage

363

Abstract

We propose new deterministic low-storage constructions of compressive sampling matrices based on classical finite-geometry generalized polygons. For the noiseless measurements case, we develop a novel exact-recovery algorithm for strictly sparse signals that utilizes the geometry properties of generalized polygons and exhibits complexity linear in the sparsity value. In the presence of measurement noise, recovery of the generalized-polygon sampled signals can be carried out effectively using a belief propagation algorithm.

Keywords

matrix algebra; signal reconstruction; signal sampling; belief propagation algorithm; classical finite-geometry generalized polygons; compressed sensing; compressive sampling matrices; exact-recovery algorithm; generalized polygon samplers; generalized-polygon sampled signals; measurement noise; sparse signals; Complexity theory; Compressed sensing; Matching pursuit algorithms; Noise; Noise measurement; Q measurement; Sparse matrices; Belief propagation; Nyquist sampling; bipartite graphs; compressed sensing; compressive sampling; finite geometry; generalized polygons; sparse signals;

fLanguage

English

Publisher

ieee

Conference_Titel

Signals, Systems and Computers (ASILOMAR), 2010 Conference Record of the Forty Fourth Asilomar Conference on

Conference_Location

Pacific Grove, CA

ISSN

1058-6393

Print_ISBN

978-1-4244-9722-5

Type

conf

DOI

10.1109/ACSSC.2010.5757535

Filename

5757535