Iterative Minimization Schemes for Solving the Single Source Localization Problem

2008 ◽  
Vol 19 (3) ◽  
pp. 1397-1416 ◽  
Author(s):  
Amir Beck ◽  
Marc Teboulle ◽  
Zahar Chikishev
Keyword(s):  
Source Localization ◽  
Single Source ◽  
Localization Problem ◽  
Iterative Minimization

scholarly journals Systematic Regularization of Linear Inverse Solutions of the EEG Source Localization Problem

NeuroImage ◽  
2002 ◽  
Vol 17 (1) ◽  
pp. 287-301 ◽  
Author(s):  
Christophe Phillips ◽  
Michael D. Rugg ◽  
Karl J. Friston
Keyword(s):  
Source Localization ◽  
Localization Problem ◽  
Inverse Solutions ◽  
Eeg Source Localization

Wideband source localization using sparse learning via iterative minimization

2013 ◽  
Vol 93 (12) ◽  
pp. 3504-3514 ◽  
Author(s):  
Luzhou Xu ◽  
Kexin Zhao ◽  
Jian Li ◽  
Petre Stoica
Keyword(s):  
Source Localization ◽  
Sparse Learning ◽  
Iterative Minimization

Application Analysis of Compressive Sensing/Sampling Theory on Source Localization

2012 ◽  
Vol 239-240 ◽  
pp. 1409-1412
Author(s):  
Xue Bing Han ◽  
Chun Hui Qiu ◽  
Zhao Jun Jiang
Keyword(s):  
Compressive Sensing ◽  
Source Localization ◽  
Sampling Theory ◽  
Restricted Isometry Property ◽  
Necessary Condition ◽  
Localization Problem ◽  
Sensing Matrix ◽  
Application Analysis

In this paper, we consider the source localization problem with Compressive Sensing/Sampling (CS) Theory. CS Theory asserts one can reconstruct sparse or compressible signals from a very limited number of measurements. A necessary condition relies on properties of the sensing matrix such as the restricted isometry property (RIP). This paper explains why sparse construction can be used in source localization with RIP conception.

Sign in / Sign up

Export Citation Format

Share Document