Abstract: GNSS integrity assessment has always been linked to the need for reliable positional information. Initially used in aviation, positional information gained even more relevance in terrestrial applications with the popularity of GNSS. However, the terrestrial environment has many influences over GNSS signals, which reduces the positional quality of tracking objects. Advances have been achieved in the use of integrity monitoring algorithms, but there are limitations to their use, especially those concerning positional accuracy in urban environments with low-cost devices. This paper aims to discuss a comparative method using two low-cost GNSS receivers designed for transportation applications and to verify whether this method can evaluate positional quality in pre-established locations, as well as the possibilities of using these devices for transportation applications, considering the positional error. Results show that, in the static experiment, the receiver assembled with a GPS antenna active embedded was more accurate than the receiver assembled with an external antenna, presenting better values in 5 out of 10 evaluated sites, while the external antenna performed better in only 2 sites. However, in a kinematic evaluation, the receiver assembled with an external antenna provided better results when considering positional error as assessment criterion, resulting in values less than or equal to 8 meters in 99.7% of the route evaluated, while the embedded antenna had 95.3%.

Abstract: At the end of 2018, the Laboratorio de Meteorología Espacial, Atmósfera Terrestre, Geodesia, Geodinámica, Diseño de Instrumental y Astrometría, from La Plata University, started providing near real-time Regional Ionosphere Maps (MAGN) with high spatial and temporal resolutions and low latency. In this paper, we show a performance evaluation of these new ionosphere maps in space of positioning during the first year (2019) of available data. The assessment was carried out through absolute GNSS positioning using the in-house RT-PPP software. Data from five stations in Brazil, distributed in different geomagnetic latitudes, were processed. The performance of the MAGN was evaluated comparing the coordinates estimated using MAGN maps and the Global Ionosphere Maps (GIM) from CODE, both concerning the PPP ionosphere-free combination solution. Although it has been found that, in general, the MAGN and CODE solutions are compatible, the former provided better positioning result (8.5%) in the altimetric (up) component. However, when an atypical behavior in the MAGN solution at the end of the year 2019 as well as days of incomplete maps were removed from the analysis, the solution with MAGN becomes slightly better than CODE, providing an improvement of 8.6% in the up component.

Uma forma de se prever o conteúdo total de elétrons na direção vertical (VTEC - Vertical Total Electron Content) usando a arquitetura de redes neurais artificiais (RNA) denominada de perceptrons de múltiplas camadas (MLP - MultipLayer Percetrons) é apresentada e avaliada nesta pesquisa. As entradas do modelo foram definidas como sendo a posição dos pontos ionosféricos (IPP - Ionospheric Pierce Point) e o tempo universal (TU), enquanto que a saída é o VTEC. As variações sazonais e de períodos mais longos são levadas em conta através da atualização do treinamento diariamente. Testes foram conduzidos sobre uma área que abrange o Brasil e sua vizinhança considerando períodos de alta e baixa atividade solar. As RNA foram treinadas utilizando informações dos mapas globais da ionosfera (GIM - Global Ionospheric Maps) produzidos pelo serviço internacional do GNSS (IGS - International GNSS Service) das 72 horas anteriores à época de início da previsão. As RNA treinadas foram utilizadas para prever o VTEC por 72 horas (VTEC RNA). Os VTEC RNA foram comparados com os VTEC contidos nos GIM (VTEC GIM). A raiz do erro médio quadrático (RMS) da diferença entre o VTEC GIM e o VTEC RNA variou de 1,4 a 10,7 unidades de TEC (TECU). O erro relativo mostra que a RNA proposta foi capaz de prever o VTEC com 70 a 85% de acerto.

An approach to predict vertical total electron content (VTEC) using multiplayer percetrons (MLP) artificial neural network (ANN) is presented. The models inputs were defined as being the ionospheric pierce point (IPP) positioning and the universal time (UT), while the output is the VTEC. The seasonal and longer period variations of the ionosphere were taken into account by daily training the ANN. Tests were conducted over an area covering Brazil and its vicinity in both high and low solar activity. The ANNs were trained using the previous 72 hours of data from the Global Ionospheric Maps (GIM) produced by the International GNSS Service (IGS). The trained ANNs were used to predict the VTEC to 72 hours (VTEC RNA). Comparisons between VTEC RNA and the VTEC contained into GIM (VTEC GIM) were made. The root mean square (RMS) of the differences between them varied from 1.4 to 10.7 TEC units (TECU). The relative error shows the ANN can correctly predict the VTEC GIM from 70 to 85%.