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

5 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.

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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