Estimação da distância de pontos móveis baseada na potência de sinais de roteadores de redes sem fio

Detalhes bibliográficos
Ano de defesa: 2017
Autor(a) principal: Cunha, Rodrigo de Lima
Orientador(a): Não Informado pela instituição
Banca de defesa: Não Informado pela instituição
Tipo de documento: Dissertação
Tipo de acesso: Acesso aberto
Idioma: por
Instituição de defesa: Universidade Federal de Lavras
Programa de Pós-Graduação em Engenharia de Sistemas e Automação
UFLA
brasil
Departamento de Engenharia
Programa de Pós-Graduação: Não Informado pela instituição
Departamento: Não Informado pela instituição
País: Não Informado pela instituição
Palavras-chave em Português:
GPS
Link de acesso: http://repositorio.ufla.br/jspui/handle/1/13239
Resumo: The need to obtain the localization of primordial technologies on the terrestrial globe surface allowed the expansion and emergence of primordial technologies to contemporary engineering, especially in the context of intelligent vehicles and their interaction with the environment. Many systems have now been based on the information obtained by GNSS (Global Navigation Satellite System) for localization. However, this system demonstrates structural limitations, which prevent users from obtaining more accurate positions, especially in closed environments such as underground parking lots, environments where satellite signals are not possible or where interference occurs. In view of this problem, positioning systems based on RSSI signal function is a support for traditional localization. The technique proposed in this work consists of using signals provided by wireless network routers, available in the infrastructure of domestic, corporate or public networks, to estimate the position based on readings of signal power received by wireless receivers of mobile devices. For this, different models were studied and evaluated that equate the distance relation as a function of the power of the RSSI signal received. In order to evaluate the best model for the scenarios studied, an optimization was applied using the Particle Swarm Optimization (PSO) algorithm to obtain the optimum parameters of each model, which would be a minimum estimation error. Experiments in real environment were performed to evaluate this solution.