Bayesian Filtering for Bluetooth RSS-based Indoor Tracking

With the technical advances of wireless sensor networking and smart mobile device, the demand for position information of pedestrians (especially in the indoor environment) has increased remarkably. In this paper, we proposed an indoor localization approach based on received signal strength (RSS) and Bayesian filter. In the following sections, we describe our virtual modeling method of environment and the way we take object's movement sequences as history conditions in Bayesian filter. The experiment results show that our solution provides accurate tracking results (within 80 centimeters for moving object). The contribution of this research is that it provides a general implementation utilizing Bayesian filter which is able to estimate location precisely with off-the-shelf hardware. And Bluetooth Low Energy (BLE) is employed which reduces power consumption considerably. Meanwhile the accuracy is sufficient for pedestrian tracking in real application scenarios where BLE devices can be easily deployed.