Full metadata record
| DC Field | Value | Language |
|---|---|---|
| dc.contributor.author | Landívar, Jerry | |
| dc.contributor.author | Ormaza, Carolina | |
| dc.contributor.author | Asanza, Víctor | |
| dc.contributor.author | Ojeda, Verónica | |
| dc.contributor.author | Avilés, Juan Carlos | |
| dc.contributor.author | Peluffo-Ordóñez, Diego H. | |
| dc.contributor.editor | Pinker, Jiří | |
| dc.date.accessioned | 2022-11-03T14:00:01Z | |
| dc.date.available | 2022-11-03T14:00:01Z | |
| dc.date.issued | 2022 | |
| dc.identifier.citation | 2022 International Conference on Applied Electronics: Pilsen, 6th – 7th September 2022, Czech Republic, p. 19-24. | en |
| dc.identifier.isbn | 978-1-6654-9482-3 | |
| dc.identifier.uri | http://hdl.handle.net/11025/49843 | |
| dc.description.sponsorship | ESPOL Research Deanate | en |
| dc.format | 6 s. | cs |
| dc.format.mimetype | application/pdf | |
| dc.language.iso | en | en |
| dc.publisher | Fakulta elektrotechnická ZČU | cs |
| dc.rights | © IEEE | en |
| dc.subject | trilaterace | cs |
| dc.subject | strojové učení | cs |
| dc.subject | RSSI | cs |
| dc.subject | vnitřní polohovací systémy | cs |
| dc.title | Trilateration-based Indoor Location using Supervised Learning Algorithms | en |
| dc.type | konferenční příspěvek | cs |
| dc.type | conferenceObject | en |
| dc.rights.access | openAccess | en |
| dc.type.version | publishedVersion | en |
| dc.description.abstract-translated | The indoor positioning system (IPS) has a wide range of applications, due to the advantages it has over Global Positioning Systems (GPS) in indoor envi- ronments. Due to the biosecurity measures established by the World Health Organization (WHO), where the social distancing is provided, being stricter in indoor environ- ments. This work proposes the design of a positioning system based on trilateration. The main objective is to predict the positioning in both the ‘x’ and ‘y’ axis in an area of 8 square meters. For this purpose, 3 Access Points (AP) and a Mobile Device (DM), which works as a raster, have been used. The Received Signal Strength Indication (RSSI) values measured at each AP are the variables used in regression algorithms that predict the x and y position. In this work, 24 regression algorithms have been evaluated, of which the lowest errors obtained are 70.322 [cm] and 30.1508 [cm], for the x and y axes, respectively | en |
| dc.subject.translated | trilateration | en |
| dc.subject.translated | machine learning | en |
| dc.subject.translated | RSSI | en |
| dc.subject.translated | Indoor Positioning Systems | en |
| dc.type.status | Peer-reviewed | en |
| Appears in Collections: | Applied Electronics 2022 Applied Electronics 2022 | |
Files in This Item:
| File | Description | Size | Format | |
|---|---|---|---|---|
| uvod.pdf | Plný text | 1,61 MB | Adobe PDF | View/Open |
| Trilateration-based_Indoor_Location_using_Supervised_Learning_Algorithms.pdf | Plný text | 2,5 MB | Adobe PDF | View/Open |
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