Smartphone-based user positioning in a multiple-user context with Wi-Fi and Bluetooth

TL;DR

This paper introduces methods to enhance multi-user indoor positioning accuracy by combining Wi-Fi and Bluetooth data, modeling their errors, and accounting for user movement over time, achieving significant error reduction.

Contribution

The authors propose novel error modeling approaches for Wi-Fi and Bluetooth data fusion in multi-user indoor positioning, improving accuracy over standard methods.

Findings

Achieved around 3.0m distance error for 75% of the time

Outperformed standard Wi-Fi fingerprinting in accuracy

Validated approaches with multiuser indoor data

Abstract

In a multiuser context, the Bluetooth data from the smartphone could give an approximation of the distance between users. Meanwhile, the Wi-Fi data can be used to calculate the user's position directly. However, both the Wi-Fi-based position outputs and Bluetooth-based distances are affected by some degree of noise. In our work, we propose several approaches to combine the two types of outputs for improving the tracking accuracy in the context of collaborative positioning. The two proposed approaches attempt to build a model for measuring the errors of the Bluetooth output and Wi-Fi output. In a non-temporal approach, the model establishes the relationship in a specific interval of the Bluetooth output and Wi-Fi output. In a temporal approach, the error measurement model is expanded to include the time component between users' movement. To evaluate the performance of the two approaches, we collected the data from several multiuser scenarios in indoor environment. The results show that the proposed approaches could reach a distance error around 3.0m for 75 percent of time, which outperforms the positioning results of the standard Wi-Fi fingerprinting model.