基于自适应蝙蝠算法的室内RFID定位算法
Indoor RFID localization algorithm based on adaptive bat algorithm
- 通信学报 2022年43卷第8期 页码：90-99
- 作者机构：
- 作者简介：
[ "谢良波（1986- ），男，四川成都人，博士，重庆邮电大学副教授、硕士生导师，主要研究方向为射频识别技术、室内定位技术等" ]
[ "李宇洋（1997- ），男，四川宜宾人，重庆邮电大学硕士生，主要研究方向为室内射频识别定位技术" ]
[ "王勇（1987- ），男，云南邵通人，博士，重庆邮电大学讲师、硕士生导师，主要研究方向为室内定位、深度学习等" ]
[ "周牧（1984- ），男，重庆人，博士，重庆邮电大学教授、博士生导师，主要研究方向为无线定位、参数估计、机器学习等" ]
[ "聂伟（1987- ），男，重庆人，博士，重庆邮电大学讲师、硕士生导师，主要研究方向为射频识别技术、室内定位技术、电磁场与微波技术等" ]
- 基金信息：国家自然科学基金资助项目;The National Natural Science Foundation of China(62101085);重庆市教委科学技术研究基金资助项目;The Science and Technology Research Program of Chongqing Municipal Education Commission(KJZD-K202000605);重庆市研究生科研创新基金资助项目;Chongqing Graduate Scientific Research Innovation Project(CYS22473);重邮信通青创团队支持计划基金资助项目;The Youth Innovation Group Support Program of ICE Discipline of CQUPT(SCIE-QN-2022-06)
DOI：10.11959/j.issn.1000-436x.2022159
中图分类号： TN99-
网络首发：2022-08，
纸质出版：2022-08-25
- 稿件说明：
移动端阅览
谢良波, 李宇洋, 王勇, 等. 基于自适应蝙蝠算法的室内RFID定位算法[J]. 通信学报, 2022,43(8):90-99.
Liangbo XIE, Yuyang LI, Yong WANG, et al. Indoor RFID localization algorithm based on adaptive bat algorithm[J]. Journal on Communications, 2022, 43(8): 90-99.
谢良波, 李宇洋, 王勇, 等. 基于自适应蝙蝠算法的室内RFID定位算法[J]. 通信学报, 2022,43(8):90-99. DOI： 10.11959/j.issn.1000-436x.2022159.
Liangbo XIE, Yuyang LI, Yong WANG, et al. Indoor RFID localization algorithm based on adaptive bat algorithm[J]. Journal on Communications, 2022, 43(8): 90-99. DOI： 10.11959/j.issn.1000-436x.2022159.
摘要
针对传统超高频射频识别室内定位算法中利用几何方法定位耗时较长且定位精度较差的问题，提出了一种基于自适应蝙蝠算法（ABA）的室内 RFID 定位算法。首先，利用跳频技术获取多频率相位，基于多重信号分类（MUSIC）算法的角度信息和聚类的距离信息建立蝙蝠算法位置评估函数；其次，利用 Tent 反向学习初始化蝙蝠位置增加种群的多样性，并引入自适应权重因子更新蝙蝠位置；最后，基于位置评估函数对目标位置进行迭代搜索，实现快速厘米级定位。实验结果表明，所提算法的中值定位误差为 7.74 cm，且实时性比基于中国剩余定理（CRT）的传统定位算法提升了12倍。
Abstract
Aiming at the problem that long time-consuming and poor positioning accuracy using geometric method in the traditional UHF RFID indoor localization algorithm
an RFID indoor positioning algorithm based on adaptive bat algorithm (ABA) was proposed.Firstly
the phase of multiple frequency points was obtained by frequency hopping technology
and the location evaluation function of bat algorithm was established based on the angle information of multiple signal classification (MUSIC) algorithm and the distance information of clustering.Secondly
the bat location was initialized by tent reverse learning to increase the diversity of the population
and the adaptive weight factor was introduced to update the bat location.Finally
the target position was searched iteratively based on the position evaluation function to achieve fast centimeter level positioning.Experimental results show that the median localization error of the proposed algorithm is 7.74 cm
and the real-time performance is improved by 12 times compared with the traditional positioning algorithm based on the Chinese remainder theorem (CRT).
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references
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