Kim, Sanghyun (Yonsei University), Seo, Jiwon (Yonsei University)

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

Scheduled for presentation during the Invited Session "S17b-Synthetic-Data-Aided Safety-Critical Scenario Understanding in ITS" (TH-EA-T17), Thursday, November 20, 2025, 13:30−13:50, Southport 2

2025 IEEE 28th International Conference on Intelligent Transportation Systems (ITSC), November 18-21, 2025, Gold Coast, Australia

This information is tentative and subject to change. Compiled on October 18, 2025

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.