Improving Indoor Positioning Systems Accuracy in Closed Buildings with Kalman Filter and Feedback Filter

摘要

In recent years, location-based technology has been widely used, one of which is the Indoor Positioning System (IPS). This technology is specifically designed for indoor use. IPS technology can be used for various purposes in various fields, for example to find the location of doctors in hospitals, to help find routes at department stores and exhibitions, or even to find objects in a room. In a closed room, sometimes the signal strength becomes unstable due to several effects called multipath and shadowing. This signal instability can cause a decrease in the position accuracy level. Filtering can be used as a solution. This research compares the performance of several filtering methods in reducing RSSI signal fluctuations so as to improve positioning accuracy. Two different Kalman Filters and the Feedback Filter are used as a comparison. The accuracy performance of each filtering method are compare. In addition the effect of multipath and shadowing are considered. The filtering method delay are also analysed. The results show that a modified Kalman Filter method has the highest accuracy in improving the IPS performance with an MSE value of 1.09.