PITM: Passive indoor object tracking with Markov probability estimation in wireless sensor networks

Exploiting Radio Signal Strength Indicator (RSSI) estimation method to conduct indoor localization in wireless sensor networks offers a new approach to serve object tracking and improve the tracking precision. When the object is closer the line-of-sight link between the transmitter and the receiver, it is more likely to arouse the attenuation or amplification of the RSSI signal. In this paper, the exponential model is used to analyze the fluctuation of the measured RSSI with the nodes, TT - CC2430, in the indoor environment. The indoor object tracking scenario is investigated through a Markov analytical model. The analysis based on Markov probability estimation lays a theoretical foundation on the object tracking process. Furthermore, PITM scheme is presented for object tracking based on the analytical results. The soundness of the analytical approach and the tracking accuracy of the PITM scheme are verified through extensive simulation, which also guides the design of more sophisticated indoor object tracking schemes.