COMPASS: An Active RFID-Based Real-Time Indoor Positioning System

Location-awareness has attracted attention over the past decades because of growing commercial interest in indoor location-based service. The k-nearest neighbor (kNN)-based positioning methods perform poorly in complex indoor environments due to ambiguity in the received signal strength indicator (RSSI). We propose a pragmatic real-time positioning framework, the Community-Optimized Measuring of Positions Associated with Sensing Signals (COMPASS), to improve positioning accuracy. This paper presents a framework to increase the probability of selecting the optimal neighbors. Our framework is implemented through an active RFID network. First, a Kalman filter (KF) is used to filter fluctuations in raw RSSIs. Second, instead of using individual tags to calculate positions, COMPASS divides the sensing area into a number of regions called “communities.” Third, a community is elected from the community chain of signal strength disparity that has the highest probability of enclosing the tracking point. Experimental results show that, compared to traditional kNN, KF-COMPASS had a 67.2% increased probability to select correct neighbors. In addition, the mean absolute error and root mean square error are 105 cm and 120 cm, which are comparable to recent studies