蝗虫群优化和极限学习机相结合的RFID室内定位算法

doi:10.11896/jsjkx.181202381

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

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

(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

(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/Abstract

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

王哲, 郑嘉利, 李丽, 袁源, 石静. 蝗虫群优化和极限学习机相结合的RFID室内定位算法[J]. 计算机科学, 2019, 46(12): 120-125.

WANG Zhe, ZHENG Jia-li, LI Li, YUAN Yuan, SHI Jing. RFID Indoor Positioning Algorithm Combining Grasshopper Optimization Algorithm and Extreme Learning Machine[J]. Computer Science, 2019, 46(12): 120-125.

参考文献 18

[1]	POTGANTWAR A D,WADHAI V M.Improved Indoor Positioning Using RSS and Directional Antenna Integrating with RFID and Wireless Technology[C]//Proceedings of International Conference on ICT for Sustainable Development.Singapore:Springer,2016:319-328.
[2]	SHEN G H,MA Y T,LIU K H,et al.An indoor RFID tag localization algorithm in NLOS environment[J].Computer Engineering and Science,2016,38(3):454-459.(in Chinese)沈郭浩,马永涛,刘开华,等.非视距环境下室内RFID标签定位算法研究[J].计算机工程与科学,2016,38(3):454-459.
[3]	TOMIC S,BEKO M.Exact Robust Solution to TW-ToA-Based Target Localization Problem With Clock Imperfections[J].IEEE Signal Processing Letters,2018,25(4):531-535.
[4]	AI Z,LIU Y.Research on the TDOA Measurement of Active RF1D Real Time Location System[C]//IEEE International Conference on Computer Science & Information Technology.IEEE,2010:410-412.
[5]	AZZOUZI S,CREMER M,DETTMAR U,et al.New measurement results for the localization of UHF RFID transponders using an Angle of Arrival (AoA) approach[C]//IEEE International Conference on Rfid.IEEE,2011:91-97.
[6]	SONG X,LI X,TANG W,et al.A hybrid positioning strategy for vehicles in a tunnel based on RFID and in-vehicle sensors[J].Sensors,2014,14(12):23095-23118.
[7]	XU K,LIU H L.Characteristics of path loss for wireless sensor networks in indoor environments[J].Transducer and Microsystem Technologies,2016,35(12):11-13.(in Chinese)徐琨,刘宏立.室内环境下无线传感网络路径衰减特性[J].传感器与微系统,2016,35(12):11-13.
[8]	ZHANG X Q,FANG F,YING K Z,et al.Design and Implement of an Interpolation Indoor Fingerprint-Based Localization System[J].Chinese Journal of Sensors and Actuators,2017,30(4):596-602.(in Chinese)章晓强,方飞,应可珍,等.一种基于插值的室内指纹定位系统设计与实现[J].传感技术学报,2017,30(4):596-602.
[9]	TANG Y,BAI Y,MA Y,et al.Research of WiFi-based Fingerprinting Matching Algorithm in Indoor Positioning[J].ComputerScience,2016,43(5):73-75.(in Chinese)唐洋,白勇,马跃,等.基于WiFi的指纹匹配算法在室内定位中的应用研究[J].计算机科学,2016,43(5):73-75.
[10]	GAO Z,MA Y,LIU K,et al.An Indoor Multi-tag Cooperative Localization Algorithm Based on NMDS for RFID[J].IEEE Sensors Journal,2017,17(7):2120-2128.
[11]	KUNG H Y,CHAISIT S,PHUONG N T M.Optimization of an RFID location identification scheme based on the neural network[J].International Journal of Communication Systems,2015,28(4):625-644.
[12]	DWIYASA F,LIM M H.Extreme Learning Machine for Active RFID Location Classification[C]//Proceedings of the 18th Asia Pacific Symposium on Intelligent and Evolutionary Systems.Springer International Publishing,2015:657-670.
[13]	ZHU Q Y,QIN A K,SUGANTHAN P N,et al.Evolutionary extreme learning machine[J].Pattern Recognition,2005,38(10):1759-1763.
[14]	CAO J,LIN Z,HUANG G B.Self-Adaptive Evolutionary Extreme Learning Machine[J].Neural Processing Letters,2012,36(3):285-305.
[15]	ABDELGAYED T S,MORSI W G,SIDHU T S.Fault Detection and Classification based on Co-Training of Semi-Supervised Machine Learning[J].IEEE Transactions on Industrial Electroni-cs,2017,PP(99):1-1.
[16]	HAN F,YAO H F,LING Q H.An improved evolutionary extreme learning machine based on particle swarm optimization[J].Neurocomputing,2013,116(1):87-93.
[17]	DONG H B,LI D M,ZHANG X P.Particle Swarm Optimization Algorithm with Dynamically Adjusting Inertia Weight[J].Computer Science,2018,45(2):98-102.(in Chinese)董红斌,李冬锦,张小平.一种动态调整惯性权重的粒子群优化算法[J].计算机科学,2018,45(2):98-102.
[18]	SAREMI S,MIRJALILI S,LEWIS A.Grasshopper Optimisation Algorithm:Theory and application[J].Advances in Engineering Software,2017,105(4):30-47.

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蝗虫群优化和极限学习机相结合的RFID室内定位算法

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

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