|Title:||Study of dynamic factors in indoor positioning for harsh environments||Authors:||de Blasio, Gabriel
García, Carmelo R.
Molina-Gil, Jezabel Miriam
|UNESCO Clasification:||3327 Tecnología de los sistemas de transporte
120304 Inteligencia artificial
|Issue Date:||2017||Publisher:||Springer||Journal:||Lecture Notes in Computer Science||Conference:||11th International Conference on Ubiquitous Computing and Ambient Intelligence, (UCAmI 2017)||Abstract:||This paper presents a study of the impact of dynamic factors on indoor positioning. A positioning system is presented that provides advanced information services based on two subsystems: Wi-Fi and Bluetooth Low Energy (BLE). The first subsystem was intended to position users with not very high levels of accuracy and precision, but not too far from reality, and the second one was intended to position users with greater precision. It is designed for use in stations and terminals of public transportation systems in which the conditions are “hostile” or unfavourable. Experimental results demonstrate that, using different devices for both offline and online phase, RSS differences, Euclidean distance and comparing fingerprints with Weighted k-Nearest Neighbours (WKNN) algorithm, the system is able to position users with reasonable values of accuracy and precision: for Wi-Fi, with only 3 samples, depending on the orientation and compared with 3 neighbours, an average accuracy between 4.15 and 4.58 m and a precision in the range 4–7 m or less 90% of the time were obtained; for BLE, best accuracy results were obtained by comparison with 2 neighbours, giving a position error of 1.59 m and a CDF value of 2.83 m or less 90% of the time.||URI:||http://hdl.handle.net/10553/52400||ISBN:||978-3-319-67584-8||ISSN:||0302-9743||DOI:||10.1007/978-3-319-67585-5_8||Source:||Ubiquitous Computing and Ambient Intelligence. UCAmI 2017. Lecture Notes in Computer Science, v. 10586 LNCS, p. 67-78|
|Appears in Collections:||Capítulo de libro|
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