Posicionamento relativo em tempo real e pós-processado utilizando microrreceptor GNSS usado em smartphone

• Autor(a) principal: Oliveira, Gabriel Diniz de
• Data de Publicação: 2014
• Tipo de documento: Dissertação
• Idioma: por
• Título da fonte: LOCUS Repositório Institucional da UFV
• Texto Completo: http://locus.ufv.br/handle/123456789/3839
• topic: Smartphone; Sistema de posicionamento global; Satélites artificiais em navegação; Sistemas de comunicação móvel; Smartphone; Global positioning system; Artificial satellites in navigation; Mobile communication systems; CNPQ::ENGENHARIAS::ENGENHARIA CIVIL

Over time has increased the need for low cost positioning of the populatio, and for this reason the demand for navigation devices has grown considerably in all levels of users of these devices, the population in general has greater access to smartphones because of its many features. Smartphones using micro GNSS receiver has a main component of positioning, where the pseudorange is its basic observable. The data sources for GPS corrections are increasingly available to the community through institutions as the IBGE with the Brazilian Network for Continuous Monitoring (RBMC). Thus, there is a greater possibility of improvement in accuracy of positioning these devices with the post- processed relative positioning and even in real time. With these possibilities and needs, this study aims the establishment of methodology that improves the accuracy in the positioning devices using micro GNSS receiver with a recursive least squares estimation with the Kalman gain matrix applied in relative positioning static by double difference of pseudorange in short baselines. To verify the effectiveness of this methodology is used SiRFstar IV micro receiver data which receives data only in L1 frequency GPS constellation. Was used reference RBMC stations to process these data. With tracking 15 minutes in known points had a mean deviation in the horizontal component of the plane coordinates 29 cm for post-processing and 98 cm for the real time processing, and, for the single point positioning average deviation was 6 meters. With this, it was concluded that the use of static methods for processing on recursive least squares estimation improves the accuracy of positioning significantly, where mobile devices that were previously given only to navigation may also be used for mapping.