A journal of IEEE and CAA , publishes high-quality papers in English on original theoretical/experimental research and development in all areas of automation
Volume 5 Issue 1
Jan.  2018

IEEE/CAA Journal of Automatica Sinica

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Article Contents
Haifeng Wu, Yang Wang and Yu Zeng, "Capture-aware Bayesian RFID Tag Estimate for Large-scale Identification," IEEE/CAA J. Autom. Sinica, vol. 5, no. 1, pp. 119-127, Jan. 2018. doi: 10.1109/JAS.2017.7510757
Citation: Haifeng Wu, Yang Wang and Yu Zeng, "Capture-aware Bayesian RFID Tag Estimate for Large-scale Identification," IEEE/CAA J. Autom. Sinica, vol. 5, no. 1, pp. 119-127, Jan. 2018. doi: 10.1109/JAS.2017.7510757

Capture-aware Bayesian RFID Tag Estimate for Large-scale Identification

doi: 10.1109/JAS.2017.7510757
Funds:

the National Natural Science Foundation of China 61762093

the 17th Batch of Young and Middle-aged Leaders in Academic and Technical Reserved Talents Project of Yunnan Province 2014HB019

More Information
  • Dynamic framed slotted Aloha algorithm is one of popular passive radio frequency identification (RFID) tag anticollision algorithms. In the algorithm, a frame length requires dynamical adjustment to achieve higher identification efficiency. Generally, the adjustment of the frame length is not only related to the number of tags, but also to the occurrence probability of capture effect. Existing algorithms could estimate both the number of tags and the probability of capture effect. Under large-scale RFID tag identification, however, the number of tags would be much larger than an initial frame length. In this scenario, the existing algorithm's estimation errors would substantially increase. In this paper, we propose a novel algorithm called capture-aware Bayesian estimate, which adopts Bayesian rules to accurately estimate the number and the probability simultaneously. From numerical results, the proposed algorithm adapts well to the large-scale RFID tag identification. It has lower estimation errors than the existing algorithms. Further, the identification efficiency from the proposed estimate is also higher than the existing algorithms.

     

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