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Measuring the Uncertainty of RFID Location Streams Based on Optimal Estimation Particle Filter

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  • Yaozong Liu
  • Fawang Han
  • Xuesong Xu
  • Hong Zhang

Abstract

Radio Frequency Identification (RFID) technology is widely used in object tracking and tracing, especially in real-time locating system (RTLS). Due to the external and internal influence of RFID systems, a lot of redundant and uncertain location streams could be generated in RFID-based RTLS applications, which could seriously affect the accuracy of estimation for RFID mobile object position and cause great difficulties in RFID-based RTLS applications. In this paper, we systematically analyzed the characteristics of RFID location streams. We then derived the optimal weight for the attributes of RFID location streams by applying information entropy based methods and used probability matrix to optimize weight attributes in location streams. We also proposed an optimal estimation particle filter algorithm (OEPF) based on traditional particle filter, which greatly reduced the data redundancy and realized online measurement for the uncertainty of RFID location streams. Finally, the experimental results showed that, compared to the existing algorithms, our algorithm effectively improved the accuracy of location estimation in ensuring the premise of real-time.

Suggested Citation

  • Yaozong Liu & Fawang Han & Xuesong Xu & Hong Zhang, 2015. "Measuring the Uncertainty of RFID Location Streams Based on Optimal Estimation Particle Filter," International Journal of Distributed Sensor Networks, , vol. 11(9), pages 758391-7583, September.
    • Handle: RePEc:sae:intdis:v:11:y:2015:i:9:p:758391
    DOI: 10.1155/2015/758391
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