  We present a neural network for mitigating biased errors in pseudoranges to
improve localization performance with data collected from mobile phones. A
satellite-wise Multilayer Perceptron (MLP) is designed to regress the
pseudorange bias correction from six satellite, receiver, context-related
features derived from Android raw Global Navigation Satellite System (GNSS)
measurements. To train the MLP, we carefully calculate the target values of
pseudorange bias using location ground truth and smoothing techniques and
optimize a loss function involving the estimation residuals of smartphone clock
bias. The corrected pseudoranges are then used by a model-based localization
engine to compute locations. The Google Smartphone Decimeter Challenge (GSDC)
dataset, which contains Android smartphone data collected from both rural and
urban areas, is utilized for evaluation. Both fingerprinting and cross-trace
localization results demonstrate that our proposed method outperforms
model-based and state-of-the-art data-driven approaches.
