Name
Abstract | ||
|---|---|---|
Radio Frequency (RF) tomographic tracking is the process of tracking moving targets by analyzing changes of attenuation in wireless transmissions. This paper presents a novel sequential Monte Carlo (SMC) method for RF tomographic tracking of a single target using a wireless sensor network. The algorithm incorporates on-line Expectation Maximization (EM) to estimate model parameters. Based on experimental measurements, we introduce a new measurement model for the attenuation caused by a target. We assess performance through numerical simulation and demonstrate that it significantly outperforms previous RF tomographic tracking procedures. © 2011 IEEE. |
Year | DOI | Venue |
|---|---|---|
2011 | 10.1109/ICASSP.2011.5947223 | ICASSP |
Keywords | Field | DocType |
expectation maximization,sensor network,sequential monte carlo,pixel,wireless sensor network,wireless sensor networks,radio frequency,data model,numerical simulation,tomography,data models | Wireless,Computer simulation,Computer science,Particle filter,Artificial intelligence,Pattern recognition,Expectation–maximization algorithm,Simulation,Algorithm,Tomography,Radio frequency,Attenuation,Wireless sensor network | Conference |
Volume | Issue | ISSN |
null | null | null |
ISBN | Citations | PageRank |
978-1-4577-0537-3 | 26 | 1.27 |
References | Authors | |
4 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yunpeng Li | 1 | 578 | 45.91 |
| Xi Chen | 2 | 29 | 1.63 |
| Mark Coates | 3 | 619 | 55.55 |
| Bo Yang | 4 | 67 | 13.56 |