Indoor Positioning System using Dynamic Model Estimation

• Ano de defesa: 2021
• Autor(a) principal: Assayag, Yuri Freitas
• Outros Autores: http://lattes.cnpq.br/6409128880667607, https://orcid.org/0000-0002-1612-306X
• Tipo de documento: Dissertação
• Tipo de acesso: Acesso aberto
• Idioma: eng
• Instituição de defesa: Universidade Federal do Amazonas; Instituto de Computação; Brasil; UFAM; Programa de Pós-graduação em Informática
• Palavras-chave em Português: Mobile devices; Indoor environments; Dynamic Model Estimation; Dynamic parameters; Bluetooth-based; CIÊNCIAS EXATAS E DA TERRA; Indoor Positioning System; Bluetooth Low Energy; Path-loss Model; Localization System; Trilateration
• Link de acesso: https://tede.ufam.edu.br/handle/tede/8241

Indoor Positioning Systems (IPSs) are used to locate mobile devices in indoor environments. Model-based IPSs have the advantage of not having an exhausting training and signal characterization of the environment, as required by the fingerprint technique. However, most model-based IPSs are done using static model parameters, treating the whole scenario as having a uniform signal propagation. This might work for most small scale experiments, but not for larger scenarios. In this work, we propose PoDME (Positioning using Dynamic Model Estimation), a model-based IPS that uses dynamic parameters that are estimated based on the location the signal was sent. More specifically, we use the set of anchor nodes that received the signal sent by the mobile node and their signal strengths, to estimate the best local values for the propagation model parameters. Also, since our solution depends highly on the selected anchor nodes to use on the position computation, we propose a novel method for choosing the three best anchor nodes. Our method is based on several data analyses executed on a large-scale, Bluetooth-based, real-world experiment and it chooses not only the nearest anchor but also the ones that benefit our least-square-based position computation. Our solution achieves a position estimation error of 3 m, which is 17% lower than the position estimates obtained by positioning models based on static parameters.