Title :
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
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;
Conference_Titel :
Signals, Systems and Computers (ASILOMAR), 2010 Conference Record of the Forty Fourth Asilomar Conference on
Conference_Location :
Pacific Grove, CA
Print_ISBN :
978-1-4244-9722-5
DOI :
10.1109/ACSSC.2010.5757535
