Quantum Support Vector Machine-Based Classification of GPS Signal Reception Conditions

TL;DR

This paper applies quantum support vector machines to classify urban GPS signal reception conditions, demonstrating improved accuracy over classical methods and highlighting the importance of data scaling.

Contribution

It introduces the first application of QSVM with a ZZ feature map for classifying GPS reception scenarios in urban environments.

Findings

QSVM outperforms classical SVM in accuracy

Proper data scaling enhances QSVM performance

Experiments conducted on datasets from two urban locations

Abstract

Global Positioning System (GPS) plays a critical role in navigation by utilizing satellite signals, but its accuracy in urban environments is often compromised by signal obstructions. Previous research has categorized GPS reception conditions into line-of-sight (LOS), non-line-of-sight (NLOS), and LOS+NLOS scenarios to enhance accuracy. This paper introduces a novel approach using quantum support vector machines (QSVM) with a ZZ feature map and fidelity quantum kernel to classify urban GPS signal reception conditions, comparing its performance against classical SVM methods. While classical SVM has been previously explored for this purpose, our study is the first to apply QSVM to this classification task. We conducted experiments using datasets from two distinct urban locations to train and evaluate SVM and QSVM models. Our results demonstrate that QSVM achieves superior classification accuracy compared to classical SVM for urban GPS signal datasets. Additionally, we emphasize the importance of appropriately scaling raw data when utilizing QSVM.