New Algorithms Based on Sigma Point Kalman Filter Technique for Multi-sensor Integrated RFID Indoor/Outdoor Positioning

The demand for seamless positioning has been significantly high. The methods of providing continuous indoor/outdoor positions seamlessly and the algorithms for smoothly transferring the estimation of positions from multiple positioning systems have attracted a great interest in the Location Based Services (LBS) research community. Most seamless positioning techniques are based on integrated methods, which usually contain nonlinear relationships in observation models. In this paper, the developments for integrating the measurements in nonlinear systems based on the Sigma Point Kalman Filter (SPKF) are introduced in order to solve the complex nonlinear problems efficiently and effectively. These developments are implemented for both vehicle navigation and pedestrian positioning applications. Recent research has suggested that continuous and metre-level position solutions can be achieved using multi-sensor integrated RFID positioning systems based on SPKF related algorithms. The Iterated Reduced SPKF (IRSPKF) using a sequential approach proposed in this paper can provide more accurate positioning results with less computational cost than other SPKF based algorithms. The potential capabilities using this new algorithm developed in multi-sensor integrated RFID positioning systems for indoor/outdoor positioning applications have been demonstrated. Keywords—RFID; GPS; Sigma Point Kalman Filter; Indoor; Integration