.png){kind=icon}
Return
A semidefinite programming approach to source localization in wireless sensor networks
.png){kind=icon}
PRE
AI
DOI：10.1109/LSP.2008.916731.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
We propose a novel approach to the source localization and tracking problem in wireless sensor networks. By applying minimax approximation and semidefinite relaxation, we transform the traditionally nonlinear and nonconvex problem into convex optimization problems for two different source localization models involving measured distance and received signal strength. Based on the problem transformation, we develop a fast low-complexity semidefinite programming (SDP) algorithm for two different source localization models. Our algorithm can either be used to estimate the source location or be used to initialize the original nonconvex maximum likelihood algorithm.
Keywords:
maximum likelihood estimation
semidefinite programming
source localization
wireless sensor network
AI Summary
Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.
Journal
IF:
9.6
Papers:
1.1W
Citations:
1.7W
Organization
Cited Papers
.png){kind=icon}
ShareSaveShareSaveShareSave
Semidefinite programming approaches for sensor network localization with noisy distance measurements
OAAI
OAAIShareSaveShareSave
View more