http://www.cnr.it/ontology/cnr/individuo/prodotto/ID276825
Forecast-driven enhancement of received signal strength (RSS)- Based localization systems (Articolo in rivista)
- Type
- Label
- Forecast-driven enhancement of received signal strength (RSS)- Based localization systems (Articolo in rivista) (literal)
- Anno
- 2013-01-01T00:00:00+01:00 (literal)
- Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#doi
- 10.3390/ijgi2040978 (literal)
- Alternative label
Barsocchi P., Chessa S., Micheli A., Gallicchio C. (2013)
Forecast-driven enhancement of received signal strength (RSS)- Based localization systems
in ISPRS international journal of geo-information
(literal)
- Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#autori
- Barsocchi P., Chessa S., Micheli A., Gallicchio C. (literal)
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- grant agreement 247950 (literal)
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- http://www.mdpi.com/2220-9964/2/4/978 (literal)
- Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#numeroVolume
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- CNR-ISTI, Pisa, Italy; Computer Science Department, University of Pisa, Italy; Computer Science Department, University of Pisa, Italy; Computer Science Department, University of Pisa, Italy (literal)
- Titolo
- Forecast-driven enhancement of received signal strength (RSS)- Based localization systems (literal)
- Abstract
- Real-time user localization in indoor environments is an important issue in ambient assisted living (AAL). In this context, localization based on received signal strength (RSS) has received considerable interest in the recent literature, due to its low cost and energy consumption and to its availability on all wireless communication hardware. On the other hand, the RSS-based localization is characterized by a greater error with respect to other technologies. Restricting the problem to localization of AAL users in indoor environments, we demonstrate that forecasting with a little user movement advance (for example, when the user is about to leave a room) provides significant benefits to the accuracy of RSS-based localization systems. Specifically, we exploit echo state networks (ESNs) fed with RSS measurements and trained to recognize patterns of user's movements to feed back to the RSS-based localization system. (literal)
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