PrNet: A Neural Network for Correcting Pseudoranges to Improve Positioning with Android Raw GNSS Measurements

TL;DR

This paper introduces PrNet, a neural network that corrects pseudorange biases in GNSS data from Android smartphones, significantly enhancing localization accuracy in diverse environments.

Contribution

PrNet is the first neural network designed specifically for pseudorange bias correction using Android raw GNSS measurements, improving smartphone localization performance.

Findings

Outperforms existing model-based and data-driven methods on GSDC dataset.

Effective in both rural and urban environments.

Improves localization accuracy with corrected pseudoranges.

Abstract

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.