叶仁杰,
钟华,
姜显扬
杭州电子科技大学通信工程学院 杭州 310018
基金项目:杭州电子科技大学2019年研究生科研创新基金，浙江省公益技术应用研究计划(2017C31055)
详细信息
作者简介:严军荣：男，1974年生，讲师，研究方向为视觉目标跟踪、无线通信网络、软件定义网络等
叶仁杰：男，1996年生，硕士生，研究方向为射频识别
钟华：男，1978年生，副教授，研究方向为信号与信息处理
姜显扬：男，1971年生，副教授，研究方向为宽带无线通信
通讯作者:严军荣 yjrcn@163.com
中图分类号:TN911计量
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被引次数:0
出版历程
收稿日期:2020-03-25
修回日期:2020-12-06
网络出版日期:2020-12-16
刊出日期:2021-07-10
Twice Labels Number Estimation Algorithm Based on Gaussian Fitting and Chebyshev Inequality
Junrong YAN,,Renjie YE,
Hua ZHONG,
Xianyang JIANG
School of Communication Engineering, Hangzhou Dianzi University, Hangzhou 310018, China
Funds:Hangzhou Dianzi University Research Innovation Fund 2019, Zhejiang Provincial Public Technology Application Research Program(2017C31055)
摘要
摘要:针对射频识别技术(RFID)系统中现有标签数量估计算法存在的估计误差大、识别时延长、时间复杂度高的问题，该文提出一种基于高斯拟合与切比雪夫不等式的标签数量2次估计算法(TLNEGC)。首先根据碰撞因子与碰撞时隙比例的关系建立碰撞模型，采用高斯函数对碰撞模型中的离散数据点进行拟合逼近获得高斯估计模型；然后利用高斯估计模型初次估计标签的数量，根据初次估计的结果判断是否需要进行2次估计，2次估计是利用切比雪夫不等式对估计区间进行2次搜索以获得最佳估计值。MATLAB仿真分析表明，该文所提TLNEGC算法的平均估计误差和总时间消耗明显低于现有的高精度标签估计算法，同时具有较低的时间复杂度和较高的稳定性。
关键词:射频识别技术/
标签估计/
ALOHA算法/
切比雪夫不等式/
高斯拟合
Abstract:In order to solve the problems of large estimation error, prolonged identification and high time complexity, which exist in tag quantity estimation algorithm in Radio Frequency IDentification (RFID) system, The Twice Labels Number Estimation algorithm based on Gaussian fitting and Chebyshev inequality (TLNEGC) is proposed. Firstly, a collision model is established based on the relationship between the collision factor and the collision time slot ratio, and a Gaussian estimation model is obtained by fitting the Gaussian function to the discrete data points. Afterward, the Gaussian estimation model is used to initially estimate the number of labels, and then according to the results of the initial estimation, judge whether a second estimation is required. The second estimation is performed by using Chebyshev's inequality to search the estimation interval twice to obtain the best estimate. The MATLAB simulation analysis indicates that the average estimation error and total time consumption of the TLNEGC algorithm are significantly lower than those of existing high-precision label estimation algorithms, and it also has lower time complexity and higher stability.
Key words:Radio Frequency IDentification (RFID)/
Labels estimation/
ALOHA algorithm/
Chebyshev inequality/
Gaussian fitting
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