Name
Abstract | ||
|---|---|---|
Automatic vehicle tracking systems ease the completion of numerous tasks in different fields. Moreover they can automatically capture information, this feature allows to perform location tasks. These systems can be implemented at airports, in shopping centers and in other large buildings; in this way, wireless network scans will serve as a basis for the creation of signal maps that can be used in indoor location systems. This work proposes an automatic people tracking system which also allows to map Wi-Fi networks in order to localize people indoor. In order to operate the system, information on vehicle movement was used to capture signal maps, with the aim of reducing the need to perform manual calibration and thus, improving the updating of information. The final location is determined by combining information provided by wireless networks, Bayesian networks are employed for this task. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1016/j.neucom.2018.07.084 | Neurocomputing |
Keywords | Field | DocType |
Neural networks,Indoor location system,Mapping and tracking | Wireless network,Wireless,Tracking system,Real-time computing,Bayesian network,Artificial intelligence,Vehicle tracking system,Mathematics,Calibration,Machine learning,Location systems | Journal |
Volume | ISSN | Citations |
338 | 0925-2312 | 0 |
PageRank | References | Authors |
0.34 | 18 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| André Sales Mendes | 1 | 0 | 2.37 |
| Gabriel Villarrubia | 2 | 183 | 24.85 |
| Javier Caridad | 3 | 4 | 0.85 |
| Daniel Hernández de La Iglesia | 4 | 9 | 1.88 |
| Juan F. De Paz | 5 | 317 | 22.52 |