Enhancing Urban GNSS Positioning Reliability via Conservative Satellite Selection Using Unanimous Voting Across Multiple Machine Learning Classifiers

TL;DR

This paper introduces a conservative satellite selection method using unanimous voting across multiple machine learning classifiers to improve GNSS positioning reliability in urban environments, effectively reducing errors caused by signal blockages and multipath effects.

Contribution

It proposes a novel satellite selection strategy based on unanimous classifier agreement, enhancing urban GNSS positioning accuracy and robustness over traditional methods.

Findings

Significantly increased positioning success rate in urban areas.

Improved receiver containment rate despite imperfect LOS/NLOS classification.

Enhanced overall positioning reliability with minimal position bound increase.

Abstract

In urban environments, global navigation satellite system (GNSS) positioning is often compromised by signal blockages and multipath effects caused by buildings, leading to significant positioning errors. To address this issue, this study proposes a robust enhancement of zonotope shadow matching (ZSM)-based positioning by employing a conservative satellite selection strategy using unanimous voting across multiple machine learning classifiers. Three distinct models - random forest (RF), gradient boosting decision tree (GBDT), and support vector machine (SVM) - were trained to perform line-of-sight (LOS) and non-line-of-sight (NLOS) classification based on global positioning system (GPS) signal features. A satellite is selected for positioning only when all classifiers unanimously agree on its classification and their associated confidence scores exceed a threshold. Experiments with real-world GPS data collected in dense urban areas demonstrate that the proposed method significantly improves the positioning success rate and the receiver containment rate, even with imperfect LOS/NLOS classification. Although a slight increase in the position bound was observed due to the reduced number of satellites used, overall positioning reliability was substantially enhanced, indicating the effectiveness of the proposed approach in urban GNSS environments.