基于Newton插值与混合灰狼优化SVR的RFID定位算法

doi:10.16182/j.issn1004731x.joss.201709007

系统仿真学报 ›› 2017, Vol. 29 ›› Issue (9): 1921-1929.doi: 10.16182/j.issn1004731x.joss.201709007

基于Newton插值与混合灰狼优化SVR的RFID定位算法

徐杨杰, 王艳, 严大虎, 纪志成

江南大学物联网技术应用教育部工程研究中心,江苏无锡 214122

收稿日期:2017-05-17 发布日期:2020-06-02
作者简介:徐杨杰(1992-),男,江苏盐城,硕士生,研究方向为基于RFID的室内定位算法。
基金资助:
国家自然科学基金(61572238),江苏省杰出青年基金(BK20160001),江苏省产学研联合创新资金-前瞻性联合研究项目(BY2016022-24)

Indoor Positioning Algorithm for RFID Based on Newton Interpolation and Support Vector Regression Optimized by Hybridizing Grey Wolf Optimization

Xu Yangjie, Wang Yan, Yan Dahu, Ji Zhicheng

Engineering Research Center of Internet of Things Technology Applications Ministry of Education, Jiangnan University, Wuxi 214122, China

Received:2017-05-17 Published:2020-06-02

摘要/Abstract

摘要： 针对RFID定位系统中,传统的LANDMARC定位算法定位精度不高且需要布置大量参考标签的问题,提出了一种基于牛顿插值和差分进化改进灰狼优化支持向量回归机(Newton-HGWOSVR)的定位算法。该算法采用高斯滤波对参考标签的采样数据进行预处理;通过牛顿插值法对其它位置的信号强度进行估值来扩充信号数据库;通过HGWOSVR构建物理位置和信号接收强度值之间的非线性关系来完成定位。实验结果表明,该算法提升了室内定位的精度,并且能减少布置参考标签的工作量,提高了室内定位方法的工作效率。

关键词: RFID, Newton插值, DE算法, GWO算法, SVR算法

Abstract: Since the traditional LANDMARC location algorithms have poor positioning accuracy and cost laborious efforts to decorate reference tags in the RFID positioning system, a novel positioning algorithm proposed is based on the Newton interpolation method and support vector regression optimized by hybridizing grey wolf optimization with differential evolution (HGWOSVR). By using the proposed algorithm, Gaussian filter was used to deal with the sampling data of the reference tags.Newton interpolation method was adopted to estimate the RSS value of other reference tags to expand database. The HGWOSVR algorithm was employed to build the nonlinear relationship between the RSS value of reference tags and their locations to predict the positioned tags. Simulation results show that the proposed algorithm performs better in terms of positioning accuracy, and reduces the workload of decorating reference tags, which improves working efficiency of the indoor location positioning method.

Key words: RFID, Newton interpolation, DE algorithm, GWO algorithm, SVR algorithm

中图分类号:

TP391.9

徐杨杰, 王艳, 严大虎, 纪志成. 基于Newton插值与混合灰狼优化SVR的RFID定位算法[J]. 系统仿真学报, 2017, 29(9): 1921-1929.

Xu Yangjie, Wang Yan, Yan Dahu, Ji Zhicheng. Indoor Positioning Algorithm for RFID Based on Newton Interpolation and Support Vector Regression Optimized by Hybridizing Grey Wolf Optimization[J]. Journal of System Simulation, 2017, 29(9): 1921-1929.

参考文献

[1] 钱志鸿, 王义君. 面向物联网的无线传感器网络综述[J]. 电子与信息学报, 2013, 35(1): 215-227.
(Qian Zhi-hong, Wang Yi-jun.Internet of Things-oriented Wireless Sensor Networks Review[J]. Journal of Electronics & Information Technology, 2013, 35(1): 215-217.)
[2] 石雪军, 纪志成. 基于射频识别的室内定位系统算法研究[J]. 系统仿真学报, 2015, 27(6): 1294-1300.
(Shi Xue-jun, Ji Zhi-cheng.Indoor Positioning System Algorithm Based on RFID[J]. Journal of System Simulation (S1004-731X), 2015, 27(6): 1294-1300.)
[3] M N Lionel, Y Liu, Y C Lau, et al.LANDMARC: Indoor location sensing using active RFID[J]. Wireless- Networks (S1022-0038), 2004, 10(6): 701-710.
[4] Jin G Y, Lu X Y, Park M S.An indoor localization mechanism using active RFID tag[C]// Proceedings of the IEEE International Conference on Sensor Networks, Ubiquitous, and Trustworthy Computing, Taiwan, China. USA: IEEE, 2008: 511-514.
[5] Zhao J, Zhao C, Yang W.An improved location algorithm for RFID system in NLOS environment[C]// Wireless and Optical Communications Conference. USA: IEEE, 2010: 1-5.
[6] Li X, Yan L, Pan W, et al.Improved Accuracy of RFID Localization Assisted by Output Power Adjustment of the Reader[M]// Internet of Things. Germany: Springer Berlin Heidelberg, 2012: 220-225.
[7] Y Zhao, L M Ni.VIRE: Active RFID-based Localization Using Virtual Reference Elimination[C]// The 2007 International Conference on Parallel Processing. USA: IEEE, 2007: 56.
[8] 张明华, 张申生, 曹健. 基于无线局域网定位的误差关键因素分析与仿真[J]. 系统仿真学报, 2009, 21(15): 4866-4869.
(Zhang Ming-hua, Zhang Shen- sheng, Cao Jian. Analysis and Simulation of Key Factors for Errors in WLAN-based Location Estimation[J]. Journal of System Simulation (S1004-731X), 2009, 21(15): 4866-4869.)
[9] Mok E, Cheung B K S. An Improved Neural Network Training Algorithm for Wi-Fi Fingerprinting Positioning[J]. ISPRS International Journal of Geo-Information (S2220-9964), 2013, 2(3): 854-868.
[10] 邓志安, 徐玉滨. 基于支持向量机回归算法的WLAN室内定位系统[J]. 仪器仪表学报, 2009, 30(6): 578-582.
(Deng Zhi-an, Xu Yu-bin.A Support Vector Regression Algorithm for Indoor Positioning in Wireless Local Area Networks[J]. Chinese Journal of Scientific Instrument, 2009, 30(6): 578-582.)
[11] 张学工. 关于统计学习理论与支持向量机[J]. 自动化学报, 2000, 26(1): 32-42.
(Zhang Xue-gong.Introduction to Statistical Learning Theory and Support Vector Machines[J]. Acta Automatica Sinica, 2000, 26(1): 32-42.)
[12] 邓志安. 基于学习算法的WLAN室内定位技术研究 [D]. 哈尔滨: 哈尔滨工业大学, 2012.
(Deng Zhi-an.Research on Learning Based WLAN Indoor Positioning Techniques [D]. Harbin, China: Harbin Institute of Technology, 2012.)
[13] 黄秋钧. 基于SVR的GSM网络侧定位技术的研究与实现 [D]. 北京: 北京邮电大学, 2013.
(Huang Qiu-jun Research and Implementation of Location Technology in GSM Networks Based on SVR [D]. Beijing,Huang Qiu-jun Research and Implementation of Location Technology in GSM Networks Based on SVR [D]. Beijing, China: Beijing University of Posts and Telecommunications, 2013.)
[14] 刘路, 王太勇. 基于人工蜂群算法的支持向量机优化[J]. 天津大学学报(自然科学与工程技术版), 2011, 44(9): 803-809.
(Liu Lu, Wang Tai-yong.Support Vector Machine Optimization Based on Artificial Bee Colony Algorithm[J]. Journal of Tianjin University (Science and Technology), 2011, 44(9): 803-809.)
[15] 张勇, 黄杰, 徐科宇. 基于PCA-LSSVR算法的WLAN室内定位方法[J]. 仪器仪表学报, 2015, 36(2): 408-414.
(Zhang Yong, Huang Jie, Xu Ke-yu.Indoor positioning algorithm for WLAN based on principal component analysis and least square support vector regression[J]. Chinese Journal of Scientific Instrument, 2015, 36(2): 408-414.)
[16] 纪昌明, 周婷, 向腾飞, 等. 基于网格搜索和交叉验证的支持向量机在梯级水电系统隐随机调度中的应用[J]. 电力自动化设备, 2014, 34(3): 125-131.
(Ji Chang-ming, Zhou Ting, Xiang Teng-fei, et al.Application of support vector machine based on grid search and cross validation in implicit stochastic dispatch of cascaded hydropower stations[J]. Electric Power Automation Equipment, 2014, 34(3): 125-131.)
[17] Mirjalili S, Mirjalili S M, Lewis A.Grey Wolf Optimizer[J]. Advances in Engineering Software (S0965-9978), 2014, 69(3): 46-61.
[18] 陈涛. 基于差分进化算法的支持向量回归机参数优化[J]. 计算机仿真, 2011, 28(6): 198-201.
(Chen Tao Parameters Optimization of Support Vector Regression Based on Differential Evolution[J]. Computer Simulation (S1006-9348), 2011, 28(6): 198-201.)
[19] Chang C C, Lin C J.LIBSVM: A library for support vector machines[J]. ACM Transactions on Intelligent Systems & Technology (S2231-1961), 2011, 2(3): 389-396.

基于Newton插值与混合灰狼优化SVR的RFID定位算法

Indoor Positioning Algorithm for RFID Based on Newton Interpolation and Support Vector Regression Optimized by Hybridizing Grey Wolf Optimization

PDF (PC)

可视化

被引次数

摘要/Abstract

引用本文

使用本文

参考文献

相关文章 3

编辑推荐

Metrics

本文评价

[1]	严大虎, 徐杨杰. 融合ZigBee的改进射频识别室内定位算法研究[J]. 系统仿真学报, 2018, 30(11): 4484-4491.
[2]	王艳, 殷富成, 纪志成, 严大虎. 基于LWR-ABCSVR的WiFi指纹定位算法[J]. 系统仿真学报, 2017, 29(6): 1193-1200.
[3]	徐小平, 白博, 钱富才. 基于改进差分进化算法的Wiener模型辨识[J]. 系统仿真学报, 2016, 28(1): 147-153.