Fusion of Multiple Mobility and Observation Models for Indoor Zoning-Based Sensor Tracking
Abstract
In this paper, we propose a novel zoning-based tracking technique that combines the sensors' mobility with a WiFi-based observation model in the belief functions framework to track the sensors in real time. The next possible destinations of the sensors are predicted, leading to a mobility model. The belief functions framework is used to propagate the previous step evidence till the current one. The mobility of the sensors, along with information from the network, are used to obtain an accurate estimation of their position. The contributions of this paper are two-fold. Firstly, it proposes new mobility models based on the transition between zones and hidden Markov models, to generate evidence on the zones of the sensors without the use of inertial measurement units. Secondly, it explores the fusion of evidence generated by the mobility models on one hand, and the observation model on the other hand. The efficiency of the proposed method is demonstrated through experiments conducted on real data in two experimental scenarios.