Name
Abstract | ||
|---|---|---|
Recent theory of compressed sensing (CS) suggested that exact recovery of an unknown sparse signal can be achieved from few measurements with overwhelming probability. In this paper, we combine CS technology with a random noise SAR and proposed the concept of random noise SAR based on CS. The block diagram of the radar system and the collected data processing procedure was presented. Theoretic analysis show that the sensing matrix of the random noise SAR exhibits good restricted isometry property (RIP).When the target scene is sparse or sparse in any basis, the random noise radar based on CS can get high accuracy image by collecting far less amount of echo data than traditional noise radar does. The conclusions are all demonstrated by simulation experiments. |
Year | DOI | Venue |
|---|---|---|
2010 | 10.1109/IGARSS.2010.5651241 | IGARSS |
Keywords | Field | DocType |
signal representation,synthetic aperture radar,sparse recovery,sparse matrices,sparse signal,sensing matrix,random noise sar,data processing,random noise radar,target scene,random noise,compressed sensing,restricted isometry property,echo,echo data collection,rip,radar imaging,probability,image reconstruction,noise,simulation experiment | Radar,Iterative reconstruction,Computer vision,Data processing,Radar imaging,Synthetic aperture radar,Computer science,Remote sensing,Artificial intelligence,Restricted isometry property,Compressed sensing,Sparse matrix | Conference |
ISSN | ISBN | Citations |
2153-6996 E-ISBN : 978-1-4244-9564-1 | 978-1-4244-9564-1 | 3 |
PageRank | References | Authors |
0.48 | 4 | 6 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Hai Jiang | 1 | 3 | 0.48 |
| bingchen zhang | 2 | 110 | 17.19 |
| yueguan lin | 3 | 10 | 2.04 |
| Wen Hong | 4 | 355 | 49.85 |
| Yirong Wu | 5 | 396 | 46.55 |
| Jin Zhan | 6 | 3 | 0.82 |