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

Yung Fu Hsu, Chu Sung Cheng, Woei Chyn Chu*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

1 Scopus citations


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

Original languageEnglish
Article number07
JournalHuman-centric Computing and Information Sciences
StatePublished - 2022


  • Kalman filter
  • Location-awareness indoor positioning
  • Radio-frequency identification (rfid)
  • Rssi path-loss model


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