计算机科学 ›› 2019, Vol. 46 ›› Issue (12): 120-125.doi: 10.11896/jsjkx.181202381

• 网络与通信 • 上一篇    下一篇

蝗虫群优化和极限学习机相结合的RFID室内定位算法

王哲, 郑嘉利, 李丽, 袁源, 石静   

  1. (广西大学计算机与电子信息学院 南宁530004);
    (广西多媒体通信与网络技术重点实验室 南宁530004)
  • 收稿日期:2018-12-13 出版日期:2019-12-15 发布日期:2019-12-17
  • 通讯作者: 王哲(1992-),男,硕士生,主要研究方向为多媒体通信网络理论与技术;郑嘉利(1979-),男,教授,主要研究方向为多媒体通信、物联网技术,E-mail:zjl@gxu.edu.cn。
  • 作者简介:李丽(1994-),女,硕士生,主要研究方向为多媒体通信网络理论与技术;袁源(1995-),女,硕士生,主要研究方向为多媒体通信网络理论与技术;石静(1992-),女,硕士生,主要研究方向为多媒体通信网络理论与技术。
  • 基金资助:
    本文受国家自然科学基金项目(61761004)资助。

RFID Indoor Positioning Algorithm Combining Grasshopper Optimization Algorithm and Extreme Learning Machine

WANG Zhe, ZHENG Jia-li, LI Li, YUAN Yuan, SHI Jing   

  1. (School of Computer,Electronics and Information,Guangxi University,Nanning 530004,China);
    (Guangxi Key Laboratory of Multimedia Communications and Network Technology,Nanning 530004,China)
  • Received:2018-12-13 Online:2019-12-15 Published:2019-12-17

摘要: 随着室内定位技术的飞速发展,射频识别(Radio Frequency Identification,RFID)技术以其非接触、快速识别等优点成为解决问题的首选方案。针对目前室内定位算法的精度容易受到标签密度和算法效率的影响及对动态环境适应性不足的问题,文中提出了一种蝗虫群优化(Grasshopper Optimization Algorithm,GOA)和极限学习机(Extreme Learning Machine,ELM)相结合的RFID室内定位算法。该算法通过蝗虫群优化对极限学习机随机产生的输入层权值和隐含层阈值进行选择,以提升极限学习机的性能,从而在离线阶段减少学习时间;利用蝗虫群算法对极限学习机参数进行优化,有效克服环境以及信号强度值变化对定位精度的影响。通过实验研究了影响算法性能的因素,并验证了算法的有效性。与BP神经网络算法(NN-Based)和非度量多维尺度算法(NMDS-RFID)相比,所提算法的定位平均误差分别降低了22.32%和20.06%,平均执行时间分别减少了58.7%和7.55%。仿真和实验结果表明,所提算法在获得更精确的定位结果的同时降低了时间成本,并对环境变化具有较好的适应性。

关键词: 射频识别, 室内定位, 接受信号强度值, 蝗虫群优化算法, 极限学习机

Abstract: With the rapid development of indoor positioning technology,radio frequency identification (RFID) technology has become the preferred solution due to its advantages of non-contact and rapid identification.However,the accuracy of existing RFID indoor positioning algorithms is easily affected by the tag density and algorithm efficiency,and environmental adaptation of existing algorithms is not strong enough.Therefore,this paper introduced an RFID indoor positioning algorithm based on the grasshopper optimization algorithm (GOA) fused with extreme learning machine (ELM).The algorithm is proposed to tune the input layer weight and hidden layer threshold biases randomly generated by the extreme learning machine,so that it can reduce learning time in the offline phase.At the same time,the algorithm can effectively resist the environmental interference and overcome the change of signal strength value on the positioning accuracy.Experiments are carried out to study the influence factors and validate the performance.Both the simulation and test experiment results show that compared with NN-based algorithm and NMDS-RFID algorithm,the average positioning error of the proposed algorithm is reduced by 22.32% and 20.06% respectively,and the average execution time is reduced by 58.7% and 7.55% respectively.GOA-ELM indoor positioning algorithm can achieve more accurate positioning results and has certain adaptability to the changes of the environment.

Key words: Radio frequency identification, Indoor positioning, Received signal strength indicator, Grasshopper optimization algorithm, Extreme learning machine

中图分类号: 

  • TP301.6
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