Análise de variância WAVELET de séries temporais com dados faltantes: avaliação do índice de cintilação ionosférica S4 em sinais de satélite GPS

• Ano de defesa: 2015
• Autor(a) principal: Oliveira, Bruno Felipe de Jesus [UNESP]
• Tipo de documento: Dissertação
• Tipo de acesso: Acesso aberto
• Idioma: por
• Instituição de defesa: Universidade Estadual Paulista (Unesp)
• Palavras-chave em Português: Computação - Matematica; Análise de séries temporais; Wavelets (Matematica); Cintiladores; Ionosfera; GPS (Programa de computador); Computer science - Mathematics
• Link de acesso: http://hdl.handle.net/11449/139388; http://www.athena.biblioteca.unesp.br/exlibris/bd/cathedra/30-05-2016/000865695.pdf

The GPS is one of the GNSS systems most commonly used due to their availability and the great development of the involved technology, providing information on positioning, navigation and time to the users. However, GPS signals transmitted by satellites are subject to several types of phenomena caused by irregularridades in the ionosphere. The ionospheric scintillation is one of these phenomena that can be described as a rapid change in the phase and amplitude of the GPS signal, leading to a weakening or even loss of the signal, depending on its intensity. The scintillation periods are related to the local time, latitude, season of the year and the level of solar activity, wherein in the equatorial region, the effects are more intense.The S4 index has been widely used in the study of ionospheric scintillation in GPS signals. However, recently the presence of other phenomena in these indices was verified and proposed its correction (S4corr). In this work, we performed an wavelet variance analysis in the time series of the S4 index to check for changes in the behavior of S4 and S4corr indexes. Due to the presence of gaps in these time series, the investigation of estimators as well as the respective confidence intervals that could be estimated even with missing data was need. From the wavelet variance analysis was possible to identify the characteristic scales of larger contribution to explain the time series behavior, or a stochastic process that best characterizes the time series of S4 and S4corr indexes. Furthermore, some probability distributions that fit the S4 e S4corr...