Orientador: Paulo de Oliveira Camargo / Banca: Jonas Rodrigues de Souza / Banca: Emanuel Paiva de Oliveira Costa / Banca: Milton Hirokazu Shimabukuro / Banca: Claudinei Rodrigues de Aguiar / Resumo: Dentre os métodos de posicionamento GNSS (Global Navigation Satellite System) utilizados pela aviação no suporte das fases de aproximação e pouso preciso de aeronaves, destacam-se o SBAS (Satellite-Based Augmentation System) e o GBAS (Ground-Based Augmentation System). O GBAS tem a capacidade de corrigir a maioria dos erros envolvidos na pseudodistância a partir do DGNSS (Differential GNSS), desde que a camada ionosférica apresente um comportamento não perturbado na região do aeroporto. Entretanto, dependendo do fluxo de ionização solar, da atividade geomagnética, do ciclo de manchas solares, do ângulo zenital do Sol e da localização geográfica, a ionosfera pode sofrer fortes perturbações, proporcionando uma ameaça à integridade do GBAS, uma vez que podem ser diferentes os efeitos ionosféricos em pequenas distâncias. Assim, investigações dos erros sistemáticos devido à camada ionosférica no GBAS tem sido objeto de estudos há alguns anos. Nesse sentido, modelos de risco ionosférico, que visam determinar a máxima decorrelação ionosférica espacial existente entre a estação GBAS e a aeronave que se aproxima num aeroporto, foram desenvolvidos ou avaliados, principalmente para o hemisfério norte, mais precisamente para o território norte-americano, onde se destaca o CONUS (Conterminous United States) Threat Model. Nessa área o comportamento da ionosfera é mais estável em comparação com o observado sobre o Brasil, localizado na região ionosférica equatorial e de baixas latitudes, qu... (Resumo completo, clicar acesso eletrônico abaixo) / Abstract: Among the methods of GNSS (Global Navigation Satellite System) positioning used by the aviation in the support of the phases of approach and precise landing of aircraft, stand out the SBAS (Satellite-Based Augmentation System) and the GBAS (Ground-Based Augmentation System). GBAS has the ability to correct most of the errors involved in pseudorange from DGNSS (Differential GNSS), provided that the ionospheric layer exhibits undisturbed behavior in the airport region. However, depending on the flow of solar ionization, geomagnetic activity, sunspot cycle, zenith angle of the sun and geographic location, the ionosphere can suffer severe disturbances, posing a threat to the integrity of the GBAS, since the ionospheric effects may be different at small distances. Thus, investigations of systematic errors due to the ionospheric layer in GBAS have been the subject of studies for some years. In this sense, ionospheric threat models, which seek to determine the maximum existing spatial ionospheric decorrelation between the GBAS station and the aircraft approaching an airport, have been developed or evaluated, especially for the northern hemisphere, more precisely to the US territory, which highlights the CONUS (Conterminous United States) Threat Model. In this area, the ionosphere behavior is more stable compared to that observed in Brazil, located in the equatorial and low latitude ionospheric region, which presents the occurrence of Equatorial Ionization Anomaly (EIA), ionospheric ... (Complete abstract click electronic access below) / Doutor

Thesis (Ph.D. (Physics & Electronics)) - Rhodes University, 2009 / A thesis submitted in partial fulfilment of the requirements for the degree of Master of Science

The ability to predict the future behavior of solar activity has become of extreme importance due to its effect on the near-Earth environment. Predictions of both the amplitude and timing of the next solar cycle will assist in estimating the various consequences of Space Weather. Several prediction techniques have been applied and have achieved varying degrees of success in the domain of solar activity prediction. These techniques include, for example, neural networks and geomagnetic precursor methods. In this thesis, various neural network based models were developed and the model considered to be optimum was used to estimate the shape and timing of solar cycle 24. Given the recent success of the geomagnetic precusrsor methods, geomagnetic activity as measured by the aa index is considered among the main inputs to the neural network model. The neural network model developed is also provided with the time input parameters defining the year and the month of a particular solar cycle, in order to characterise the temporal behaviour of sunspot number as observed during the last 10 solar cycles. The structure of input-output patterns to the neural network is constructed in such a way that the network learns the relationship between the aa index values of a particular cycle, and the sunspot number values of the following cycle. Assuming January 2008 as the minimum preceding solar cycle 24, the shape and amplitude of solar cycle 24 is estimated in terms of monthly mean and smoothed monthly sunspot number. This new prediction model estimates an average solar cycle 24, with the maximum occurring around June 2012 [± 11 months], with a smoothed monthly maximum sunspot number of 121 ± 9.

This study presents the implementation of Middle East Technical University Neural Network (METU-NN) models for the ionospheric forecasting together with worldwide usage capability of the Internet. Furthermore, an attempt is made to include expert information in the Neural Network (NN) model in the form of neurofuzzy network (NFN). Middle East Technical University Neurofuzzy Network (METU-NFN) modeling approach is developed which is the first attempt of using a neurofuzzy model in the ionospheric forecasting studies. The Web based applications developed in this study have the ability to be customized such that other NN and NFN models including METU-NFN can also be adapted. The NFN models developed in this study are compared with the previously developed and matured METU-NN models. At this very early stage of employing neurofuzzy models in this field, ambitious objectives are not aimed. Applicability of the neurofuzzy systems on the ionospheric forecasting studies is only demonstrated. Training and operating METU-NN and METU-NFN models under equal conditions and with the same data sets, the cross correlation of obtained and measured values are 0.9870 and 0.9086 and the root mean square error (RMSE) values of 1.7425 TECU and 4.7987 TECU are found by operating METU-NN and METU-NFN models respectively. The results obtained by METU-NFN model is close to those found by METU-NN model. These results are reasonable enough to encourage further studies on neurofuzzy models to benefit from expert information. Availability of these models which already attracted intense international attention will greatly help the related scientific circles to use the models. The models can be architecturally constructed, trained and operated on-line. To the best of our knowledge this is the first application that gives the ability of on-line model usage with these features. Applicability of NFN models to the ionospheric forecasting is demonstrated. Having ample flexibility the constructed model enables further developments and improvements. Other neurofuzzy systems in the literature might also lead to better achievements.