RFID标签冲突分离的最大后验概率聚类  

Maximum Posteriori Probability Clustering for Conflict Separation of RFID Tags

作　　者：郭佳雯 吴海锋[1,2] 桂妮霞 吴晓刚 曾玉 陈跃斌[1] GUO Jiawen;WU Haifeng;GUI Nixia;WU Xiaogang;ZENG Yu;CHEN Yuebin(School of Electrical Information Engineering,Yunnan Minzu University,Kunming 650500,China;Scientific and Technological Innovation Team of Intelligent Sensor Network and Information System in Colleges and Universities of Yunnan Province,Kunming 650500,China)

机构地区：[1]云南民族大学电气信息工程学院,昆明650500 [2]云南省高校智能传感网络及信息系统科技创新团队,昆明650500

出　　处：《数据采集与处理》2022年第6期1333-1344,共12页Journal of Data Acquisition and Processing

基　　金：国家自然科学基金项目(62161052);云南省应用基础研究重点项目(2018FA036);2021年云南省教育厅科学研究基金项目(2021Y655)。

摘　　要：无线射频识别(Radio frequency identification,RFID)通信系统中,当多个标签冲突时,可对冲突信号分离再解码以提高系统的通信效率,而信号分离通常依靠聚类完成。然而,有时传统方法在算法时间复杂度和聚类准确度方面不可兼顾。本文提出一种最大后验概率估计的聚类方法,通过蒙特卡罗方法快速找到峰值,以此作为聚类中心完成信号分离。实验分别采用了仿真和软件无线电的实测数据对算法进行测试,两者的结果均表明,本文算法在高信噪比下具有较高的聚类准确度和较低的时间复杂度,且嵌入动态帧ALOHA系统中,吞吐量可达到0.55,高于纯动态帧ALOHA。In the radio frequency identification(RFID)communication system,when multiple tags conflict,the conflicting signals can be separated and then decoded to improve the communication efficiency,and the signal separation usually depends on clustering.However,the traditional methods cannot consider the time complexity and clustering accuracy.In this paper,a clustering method of maximum posterior probability estimation is proposed.The peak value is quickly found out by the Monte Carlo method that is used as the clustering center to complete signal separation.In the experiment,the simulation data and the measured data of software radio are used to test the proposed algorithm,and the results show that the proposed algorithm has a higher clustering accuracy and a lower time complexity under high signal noise ratio(SNR).It is embedded in dynamic frame ALOHA system,and the throughput can reach 0.55,higher than that of the pure dynamic frame ALOHA.

关 键 词：无线射频识别 标签冲突 信号分离 聚类中心 FM0码 

分 类 号：TN911[电子电信—通信与信息系统]