DocumentCode :
3625021
Title :
Cost-Based Monte Carlo Sampling Approaches for Sensor Self-Localization Under Beacon Position Uncertainty
Author :
Mahesh Vemula;Monica F. Bugallo;Petar M.Djuric
Author_Institution :
Deptartment of Electrical and Computer Engineering, Stony Brook University, Stony Brook, NY, 11794-2350. e-mail: vema@ece.sunysb.edu
Volume :
2
fYear :
2007
fDate :
4/1/2007 12:00:00 AM
Abstract :
Sensor localization methods based on Monte Carlo sampling approximate the sensor position distributions by a weighted set of samples. These approaches traditionally require complete knowledge of the probabilistic distributions of the uncertainties in the sensor system. In this paper, we propose alternative sampling-based methods which do not require complete knowledge of the probabilistic distributions. The sensor position distributions are represented by a set of samples and costs which are described by spatial parametric regions. Few parameters are needed to characterize these regions, and therefore the amount of information to be transmitted to the rest of the sensors to self-localize is simplified. Computer simulations show that the proposed methods are more robust and less computationally intensive than standard sampling approaches.
Keywords :
"Monte Carlo methods","Uncertainty","Sensor phenomena and characterization","Sensor systems","Costs","Broadcasting","Distributed computing","Bayesian methods","Sampling methods","Signal resolution"
Publisher :
ieee
Conference_Titel :
Acoustics, Speech and Signal Processing, 2007. ICASSP 2007. IEEE International Conference on
ISSN :
1520-6149
Print_ISBN :
1-4244-0727-3
Electronic_ISBN :
2379-190X
Type :
conf
DOI :
10.1109/ICASSP.2007.366365
Filename :
4217538
Link To Document :
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