  This work presents a new approach for detection and exclusion (or
de-weighting) of pseudo-range measurements from the Global Navigation Satellite
System (GNSS) in order to improve the accuracy of single-epoch positioning,
which is an essential prerequisite for maintaining good navigation performance
in challenging operating contexts (e.g., under Non-Line of Sight and/or
multipath propagation). Beyond the usual preliminary hard decision stage, which
can mainly reject obvious outliers, our approach exploits machine learning to
optimize the relative contributions from all available satellites feeding the
positioning solver. For this, we construct a customized matrix of pseudorange
residuals that is used as an input to the proposed longshort term memory neural
network (LSTM NN) architecture. The latter is trained to predict several
quality indicators that roughly approximate the standard deviations of
pseudo-range errors, which are further integrated in the calculation of
weights. Our numerical evaluations on both synthetic and real data show that
the proposed solution is able to outperform conventional weighting and signal
selection strategies from the state-of-theart, while fairly approaching optimal
positioning accuracy.
