  In the Global Navigation Satellite System (GNSS) context, the growing number
of available satellites has lead to many challenges when it comes to choosing
the most accurate pseudorange contributions, given the strong impact of biased
measurements on positioning accuracy, particularly in single-epoch scenarios.
This work leverages the potential of machine learning in predicting link-wise
measurement quality factors and, hence, optimize measurement weighting. For
this purpose, we use a customized matrix composed of heterogeneous features
such as conditional pseudorange residuals and per-link satellite metrics (e.g.,
carrier-to-noise power density ratio and its empirical statistics, satellite
elevation, carrier phase lock time). This matrix is then fed as an input to a
recurrent neural network (RNN) (i.e., a long-short term memory (LSTM) network).
Our experimental results on real data, obtained from extensive field
measurements, demonstrate the high potential of our proposed solution being
able to outperform traditional measurements weighting and selection strategies
from state-of-the-art.
