  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.
