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In a multiuser context, the Bluetooth data from the smartphone could give anapproximation of the distance between users. Meanwhile, the Wi-Fi data can beused to calculate the user's position directly. However, both the Wi-Fi-basedposition outputs and Bluetooth-based distances are affected by some degree ofnoise. In our work, we propose several approaches to combine the two types ofoutputs for improving the tracking accuracy in the context of collaborativepositioning. The two proposed approaches attempt to build a model for measuringthe errors of the Bluetooth output and Wi-Fi output. In a non-temporalapproach, the model establishes the relationship in a specific interval of theBluetooth output and Wi-Fi output. In a temporal approach, the errormeasurement model is expanded to include the time component between users'movement. To evaluate the performance of the two approaches, we collected thedata from several multiuser scenarios in indoor environment. The results showthat the proposed approaches could reach a distance error around 3.0m for 75percent of time, which outperforms the positioning results of the standardWi-Fi fingerprinting model.

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