Robust Distributed Network Localization with Noisy Range Measurements

Robust Distributed Network Localization with Noisy Range Measurements (2004)

Cached

Download Links

Other Repositories/Bibliography

DBLP

by David Moore , John Leonard , Daniela Rus , Seth Teller

Citations:

223 - 19 self

Versions

Version History

Metadata Version 2

User correction supplied by SystemCorrections

Datum	Value	Source
TITLE	Robust Distributed Network Localization with Noisy Range Measurements	user correction - Legacy Corrections
AUTHOR NAME	David Moore	SVM HeaderParse 0.1
AUTHOR AFFIL	MIT Computer Science and Artificial Intelligence Laboratory	SVM HeaderParse 0.2
AUTHOR ADDR	The Stata Center, 32 Vassar Street; Cambridge, MA 02139	SVM HeaderParse 0.1
AUTHOR NAME	John Leonard	SVM HeaderParse 0.1
AUTHOR AFFIL	MIT Computer Science and Artificial Intelligence Laboratory	SVM HeaderParse 0.2
AUTHOR ADDR	The Stata Center, 32 Vassar Street; Cambridge, MA 02139	SVM HeaderParse 0.1
AUTHOR NAME	Daniela Rus	SVM HeaderParse 0.1
AUTHOR AFFIL	MIT Computer Science and Artificial Intelligence Laboratory	SVM HeaderParse 0.2
AUTHOR ADDR	The Stata Center, 32 Vassar Street; Cambridge, MA 02139	SVM HeaderParse 0.1
AUTHOR NAME	Seth Teller	SVM HeaderParse 0.1
AUTHOR AFFIL	MIT Computer Science and Artificial Intelligence Laboratory	SVM HeaderParse 0.2
AUTHOR ADDR	The Stata Center, 32 Vassar Street; Cambridge, MA 02139	SVM HeaderParse 0.1
ABSTRACT	This paper describes a distributed, linear-time algorithm for localizing sensor network nodes in the presence of range measurement noise and demonstrates the algorithm on a physical network. We introduce the probabilistic notion of robust quadrilaterals as a way to avoid flip ambiguities that otherwise corrupt localization computations. We formulate the localization problem as a two-dimensional graph realization problem: given a planar graph with approximately known edge lengths, recover the Euclidean position of each vertex up to a global rotation and translation. This formulation is applicable to the localization of sensor networks in which each node can estimate the distance to each of its neighbors, but no absolute position reference such as GPS or fixed anchor nodes is available. We implemented the algorithm on a physical sensor network and empirically assessed its accuracy and performance. Also, in simulation, we demonstrate that the algorithm scales to large networks and handles real-world deployment geometries. Finally, we show how the algorithm supports localization of mobile nodes.	SVM HeaderParse 0.1
YEAR	2004	user correction - Legacy Corrections
CITATIONS	23 found	ParsCit 1.0