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A preliminary analysis of Galileo F/NAV broadcast Clock and Ephemeris is performed in this paper with 43 months of data. Using consolidated Galileo Receiver Independent Exchange (RINEX) navigation files, automated navigation data monitoring is applied from 1 January 2017 to 31 July 2020 to detect and verify potential faults in the satellite broadcast navigation data. Based on these observation results, the Galileo Signal-in-Space is assessed, and the probability of satellite failure is estimated. The Galileo nominal ranging accuracy is also characterized. Results for GPS satellites are included in the paper to compare Galileo performances with a consolidated constellation. Although this study is limited by the short observation period available, the analysis over the last three-year window shows promising results with Psat= 3.2 × 10−6/sat, which is below the value of 1 × 10−5 stated by the Galileo commitments.

UPC). He co-authors 2 journal articles and 14 works in international meeting proceedings. He is the responsible for the upgrades of the ESA/UPC gLAB tool suite (gLAB), including ionospheric and tropospheric models and SBAS processing. He is currently developing a global monitoring system for the performance assessment of EGNOS and Signal in Space (SIS) anomaly investigation for Feared Events activities.Adrià Rovira-Garcia is a post-doctoral researcher at UPC with a Marie Sklodowska Curie Individual Fellow titled "High Accuracy Navigation under Scintillation Conditions (NAVSCIN)". He co-authors 11 papers in peer-reviewed journals, two book chapters and over 25 works in meeting proceedings, with one best presentation award from the US Institute of Navigation and one Outstanding Poster Award from the European Geosciences Union.

In this work, a fuzzy logic system for improve the resolution in the estimation of the direction of arrival (DOA) in mobile communications, implemented on FPGA is proposed. The DOA estimation is based on a linear sensor array and a low resolution algorithm known as periodogram. The application is focused on mobile scenarios when two sources interfere between them in a spatial way over the sensor array. In order to gain robustness, system is trained based on the ANFIS architecture and a set of training data obtained from a channel model which take into account multipath effects and SNR variations in the signals detected by the sensors.

Monitoring spatiotemporal variations of ionospheric Vertical Total Electron Content (VTEC) is crucial for space weather and satellite positioning. In the present study, an Enhanced Neural Network (ENN) model is proposed to capture the changing characteristics of ionospheric VTEC and compared with the traditional mathematical models, i.e., the POLYnomial (POLY) model, Generalized Trigonometric Series Function (GTSF) and Spherical Harmonic Function (SHF) model. The ionospheric VTEC data obtained from 31 permanent Global Positioning System (GPS) stations in the southwest region of China on 26 August and 8 September, 2017, were used to test the performance of the mentioned models under different Solar-geomagnetic conditions. The ENN model is derived from the ensemble learning method, and the disadvantage that simple backpropagation neural network (BPNN) learners that are not robust enough is weakened by the ENN model. After statistical analysis and Single-Frequency Precise Point Positioning (SF-PPP) experiments, it is demonstrated that the ENN model is superior to the above three mathematical models, regardless of the solar-geomagnetic conditions. In terms of Mean Absolute Error (MAE), Root Mean Square Error (RMSE), Standard Deviation (STD), and Mean Absolute Percentage Error (MAPE), the ENN model outperforms the SHF model, which is the best mathematical model in the analysis, by 40.7%, 30.20%, 29.88%, 38.04% under quiet solar-geomagnetic conditions, and by 37.66%, 29.93%, 30.96%, 32.01% under active solar-geomagnetic conditions. In addition, the accuracy of the SF-PPP is greatly affected by the error caused by ionosphere. In the static SF-PPP experiment of this study, the ENN model can better correct ionospheric error. Under quiet and active solar-geomagnetic conditions, the SF-PPP accuracy can be improved by 85.1% and 85.2% with the ionosphere delay correction from the ENN model