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

• Ano de defesa: 2017
• Autor(a) principal: Cunha, Rodrigo de Lima
• 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
• Palavras-chave em Português: Sistemas de posicionamento; Sistemas de localização; Sistema de posicionamento diferencial; GPS; Positioning systems; Location systems; Differential positioning system; Engenharia de Software
• Link de acesso: http://repositorio.ufla.br/jspui/handle/1/13239

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.