Journal of Computer Applications ›› 2018, Vol. 38 ›› Issue (11): 3287-3292.DOI: 10.11772/j.issn.1001-9081.2018040854

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RFID tag number estimation algorithm based on sequential linear Bayes method

WANG Shuai, YANG Xiaodong   

  1. School of Electrical Engineering and Automation, Henan Polytechnic University, Jiaozuo Henan 454000, China
  • Received:2018-04-25 Revised:2018-06-02 Online:2018-11-10 Published:2018-11-10
  • Supported by:
    This work is partially supported by the Key Scientific Research Projects in Henan Colleges and Universities (17A510002), the Henan Innovation and Entrepreneurship Training Project (201710460007).


王帅, 杨晓东   

  1. 河南理工大学 电气工程与自动化学院, 河南 焦作 454000
  • 通讯作者: 王帅
  • 作者简介:王帅(1974-),男,黑龙江齐齐哈尔人,副教授,博士,主要研究方向:射频识别、无线通信;杨晓东(1996-),女,河南新乡人,硕士研究生,主要研究方向:射频识别、无线通信。
  • 基金资助:

Abstract: In order to solve the contradiction between the estimation precision and the complexity of the existing tag number estimation algorithm, a Radio Frequency IDentification (RFID) tag number estimation algorithm based on sequential linear Bayes was proposed by the analysis and comparison of the existing algorithms. Firstly, a linear model for estimating the number of tags was established based on linear Bayesian theory. This model made full use of the amount and correlation of idle, successful and collision time slots. Then, the closed form expression of the tag number estimation was derived, and the sequential solution method of the statistics was given. Finally, the computational complexity of the sequential Bayesian algorithm was analyzed and compared. The simulation results show that the proposed algorithm improves the estimation accuracy and recognition efficiency by the sequential Bayesian method. The error is only 4% when the number of time slots is half of the frame length. The algorithm updates the estimated value of the number of tags in a linear analytic form to avoid the exhaustive search. Compared with the maximum posterior probability and Mahalanobis distance algorithm with high precision, the computational complexity is reduced from O(n2) and O(n) to O(1). Through theoretical analysis and simulation, the RFID tag number estimation algorithm based on sequential linear Bayes has both high precision and low complexity, and can meet the actual estimation requirements with hardware resource constraints.

Key words: Radio Frequency IDentification (RFID), anti-collision, tag number estimation, sequential linear Bayes, Dynamic Frame Slotted-ALOHA (DFSA)

摘要: 为解决现有标签数量估计算法中估计精度与复杂度之间的矛盾,在分析比较现有算法的基础上,提出一种基于序贯线性贝叶斯的射频识别(RFID)标签数量估计算法。首先,基于线性贝叶斯理论,充分利用空闲、成功和碰撞时隙数量观测值及相关性,建立了标签数量估计问题的线性模型;然后,推导了标签数量估计值的闭式表达式,给出了表达式各阶统计量的序贯式求解方法;最后,对序贯式贝叶斯算法的计算复杂度进行了分析和对比。仿真结果表明,所提算法通过序贯贝叶斯方法提高了估计精度和识别效率,当观测时隙数为帧长一半时估计误差仅为4%。该算法以线性解析式形式更新标签数量估计值,避免了穷举搜索,与高精度的最大后验概率和马氏距离算法相比,计算复杂度由On2)和On)下降为O(1)。经理论分析和仿真验证,基于序贯线性贝叶斯的RFID标签数量估计算法兼具高精度和低复杂度的特性,能很好地满足硬件资源受限应用场景下对标签数量的估计需求。

关键词: 射频识别, 防碰撞, 标签数量估计, 序贯线性贝叶斯, 动态帧时隙ALOHA

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