A Novel GCN based Indoor Localization System with Multiple Access Points

TL;DR

This paper introduces a GCN-based indoor localization system that models access points as a graph and uses RSSI fingerprints, achieving significant accuracy improvements in 2D and 3D scenarios over existing methods.

Contribution

The novel approach models APs as a graph and applies GCN for feature extraction, enhancing indoor localization accuracy with combined RSSI fingerprints and graph learning.

Findings

Mean distance error of 11m in 2D, outperforming DNN and CNN.

Building and floor prediction accuracies up to 99.73% and 93.43%.

In 3D, mean error of 13m when floor and building are correctly predicted.

Abstract

With the rapid development of indoor location-based services (LBSs), the demand for accurate localization keeps growing as well. To meet this demand, we propose an indoor localization algorithm based on graph convolutional network (GCN). We first model access points (APs) and the relationships between them as a graph, and utilize received signal strength indication (RSSI) to make up fingerprints. Then the graph and the fingerprint will be put into GCN for feature extraction, and get classification by multilayer perceptron (MLP).In the end, experiments are performed under a 2D scenario and 3D scenario with floor prediction. In the 2D scenario, the mean distance error of GCN-based method is 11m, which improves by 7m and 13m compare with DNN-based and CNN-based schemes respectively. In the 3D scenario, the accuracy of predicting buildings and floors are up to 99.73% and 93.43% respectively. Moreover, in the case of predicting floors and buildings correctly, the mean distance error is 13m, which outperforms DNN-based and CNN-based schemes, whose mean distance errors are 34m and 26m respectively.