Name
Abstract | ||
|---|---|---|
Certainty based reduced sparse solution (CARSS) reduces the solution space of the source localization problem to the most certainly active sources. CARSS estimates the sources by utilizing (i) the extrema and the source distribution in the measurement vector, and (ii) the neighborhood continuity among measurement channels and sources. The near active sources will be interfering with each other causing the shift of extrema. The interference of the sources may eliminate the active source. In this paper, (1) The method is extended to densely and less sparse problems. (2) The reformulation of the problem to the mixed norms is proposed. (3) The methodology to de-interfere the interfered source signatures is proposed. The extension is found to be robust to sparsity as well as to the unknown additive noise. |
Year | DOI | Venue |
|---|---|---|
2021 | 10.1109/IECON48115.2021.9589118 | IECON 2021 - 47TH ANNUAL CONFERENCE OF THE IEEE INDUSTRIAL ELECTRONICS SOCIETY |
Keywords | DocType | ISSN |
electroencephalograph, source localization, inverse problem, linear model, sparse signal reconstruction, underdetermined system | Conference | 1553-572X |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
2 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Teja Mannepalli | 1 | 0 | 0.68 |
| Aurobinda Routray | 2 | 337 | 52.80 |