Three-Dimension Indoor Positioning Algorithms Using an Integrated RFID/INS System in Multi-storey Buildings
Location based services (LBS) require a reliable, accurate and continuous position determination of mobile users. This is particularly true in indoor environments where the widely used Global Positioning System ( GPS) is not available due to its signal outages. One solution is to integrate different techniques in a multi-sensor positioning system to overcome the limitations of a single sensor. In this chapter an approach is described using a three-dimensional Radio Frequency Identifi cation (3D RFID) location fi ngerprinting probabilistic approach with map-based constraints in order to provide reliable positions in indoor 3D environments. An Extended Kalman Filter (EKF) is used to integrate 3D RFID positioning method with an Inertial Navigation System (INS) in order to produce an accurate and continuous positioning estimation.
The multi-storey experiments conducted at RMIT University, Australia, show that the 3D RFID positioning method can determine the mobile user’s movements in a kinematic mode to meter-level by using the fi ngerprinting probabilistic approach. The smoothing method and the RFID/INS integration can both improve the positioning accuracy by tackling the RSS instability problem. Besides the 100Hz updating rate, the RFID/INS integration method can provide more reliable estimation based on the mobile user’s kinematic characteristics rather than simply smoothing the estimations. The results also show that the algorithms for the Integrated RFID/INS indoor positioning system developed can satisfy the requirements for personal navigation services.
Keywords3D indoor positioning RFID positioning INS Extended Kalman Filter (EKF)
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