Análise de séries temporais de coordenadas estimadas com GPS: uma proposta metodológica para detecção, remoção e recuperação de efeitos sazonais

• Autor(a) principal: Rosa, Guilherme Poleszuk Dos Santos [UNESP]
• Data de Publicação: 2009
• Outros Autores: Monico, João Francisco Galera [UNESP], Chaves, João Carlos [UNESP]
• Tipo de documento: Artigo
• Idioma: por
• Título da fonte: Repositório Institucional da UNESP
• Texto Completo: http://ojs.c3sl.ufpr.br/ojs/index.php/bcg/article/view/15516; http://hdl.handle.net/11449/180896
• topic: GPS active networks; Least Square Method; Precise Point Positioning; Seasonal effects; Wavelets; accuracy assessment; coordinate; data processing; detection method; estimation method; experimental study; Fourier transform; geodesy; geodetic network; GPS; least squares method; positioning; spectral analysis; time series analysis; wavelet analysis

GPS active networks are more and more used in geodetic surveying and scientific experiments, as water vapor monitoring in the atmosphere and lithosphere plate movement. Among the methods of GPS positioning, Precise Point Positioning (PPP) has provided very good results. A characteristic of PPP is related to the modeling and / or estimation of the errors involved in this method. The accuracy obtained for the coordinates can reach few millimeters. Seasonal effects can affect such accuracy if they are not consistent treated during the data processing. Coordinates time series analyses have been realized using Fourier or Harmonics spectral analyses, wavelets, least squares estimation among others. An approach is presented in this paper aiming to investigate the seasonal effects included in the stations coordinates time series. Experiments were carried out using data from stations Manaus (NAUS) and Fortaleza (BRFT) which belong to the Brazilian Continuous GPS Network (RBMC). The coordinates of these stations were estimated daily using PPP and were analyzed through wavelets for identification of the periods of the seasonal effects (annual and semi-annual) in each time series. These effects were removed by means of a filtering process applied in the series via the least squares adjustment (LSQ) of a periodic function. The results showed that the combination of these two mathematical tools, wavelets and LSQ, is an interesting and efficient technique for removal of seasonal effects in time series.