Compressive Sampling With Generalized Polygons

被引：19

作者：

Gao, Kanke ^{[1
]}

Batalama, Stella N. ^{[1
]}

Pados, Dimitris A. ^{[1
]}

Suter, Bruce W. ^{[2
]}

机构：

[1] SUNY Buffalo, Dept Elect Engn, Buffalo, NY 14260 USA

[2] USAF, Res Lab, RITB, Rome, NY 13441 USA

来源：

IEEE TRANSACTIONS ON SIGNAL PROCESSING | 2011年 / 59卷 / 10期

关键词：

Belief propagation; bipartite graphs; compressed sensing; compressive sampling; finite geometry; generalized polygons; low-density parity-check codes; Nyquist sampling; sparse signals; SIGNAL RECOVERY;

D O I：

10.1109/TSP.2011.2160860

中图分类号：

TM [电工技术]; TN [电子技术、通信技术];

学科分类号：

0808 ; 0809 ;

摘要：

We consider the problem of compressed sensing and 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 that depends on the sparsity value only. In the presence of measurement noise, recovery of the generalized-polygon sampled signals can be carried out effectively using a belief propagation algorithm. Experimental studies included in this paper illustrate our theoretical developments.

引用

页码：4759 / 4766

页数：8

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