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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Savic, V. Zazo, S. |
| Copyright Year | 2009 |
| Description | Author affiliation: Signal Processing Application Group, Polytechnic University of Madrid, Ciudad Universitaria S/N, 28040 Spain (Savic, V.; Zazo, S.) |
| Abstract | Belief propagation (BP) is one of the bestknown graphical model for inference in statistical physics, artificial intelligence, computer vision, etc. Furthermore, a recent research in distributed sensor network localization showed us that BP is an efficient way to obtain sensor location as well as appropriate uncertainty. However, BP convergence is not guaranteed in a network with loops. In this paper, we propose localization using generalized belief propagation based on junction tree method (GBP-JT) and nonparametric (particle-based) approximation of this algorithm (NGBP-JT). We illustrate it in a network with loop where BP shows poor performance. In fact, we compared estimated locations with Nonparametric Belief Propagation (NBP) algorithm. According to our simulation results, GBP-JT resolved the problems with loops, but the price for this is unacceptable large computational cost. Therefore, our approximated version of this algorithm, NGBP-JT, reduced significantly this cost, with little effect on accuracy. |
| Starting Page | 1966 |
| Ending Page | 1973 |
| File Size | 486814 |
| Page Count | 8 |
| File Format | |
| ISBN | 9780982443804 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2009-07-06 |
| Publisher Place | USA |
| Access Restriction | Subscribed |
| Rights Holder | ISIF |
| Subject Keyword | Belief propagation Intelligent sensors Graphical models Physics Artificial intelligence Computer vision Uncertainty Convergence Approximation algorithms Computational modeling loops Localization generalized belief propagation junction tree particle filters |
| Content Type | Text |
| Resource Type | Article |
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