计算机科学 ›› 2022, Vol. 49 ›› Issue (6A): 606-611.doi: 10.11896/jsjkx.210700108

• 计算机网络 • 上一篇    下一篇

基于无迹粒子滤波的WiFi-PDR融合室内定位技术

周楚霖, 陈敬东, 黄凡   

  1. 武汉数字工程研究所 武汉 430000
  • 出版日期:2022-06-10 发布日期:2022-06-08
  • 通讯作者: 周楚霖(952811005@qq.com)
  • 基金资助:
    山东省重点研发计划(2020CXGC010701)

WiFi-PDR Fusion Indoor Positioning Technology Based on Unscented Particle Filter

ZHOU Chu-lin, CHEN Jing-dong, HUANG Fan   

  1. Wuhan Digital Engineering Research Institute,Wuhan 430000,China
  • Online:2022-06-10 Published:2022-06-08
  • About author:ZHOU Chu-lin,born in 1996,master,engineer,is a member of China Compu-ter Federation.His main research in-terests include information fusion and artificial intelligence.
  • Supported by:
    Key R & D Program of Shandong Province,China(2020CXGC010701).

摘要: 为提高室内定位的精度和稳定度,文中提出了一种基于无迹粒子滤波的WiFi-PDR融合的室内定位方法。为降低室内复杂环境对WiFi定位的影响,采用加权路径损失算法改善WiFi定位;为降低行人航迹推算误差累积效应,通过设定参考值划分行走周期并对加速度数据进行平滑降噪处理,提高了步数计量的精度;在改进WiFi和PDR定位的基础上,提出使用无迹粒子滤波融合定位方法,并对粒子滤波进行抗差自适应优化,提高其鲁棒性。实验仿真结果表明该方法可以有效提高室内定位的精度和稳定性。

关键词: PDR, WiFi定位, 室内定位, 无迹粒子滤波, 信息融合

Abstract: In order to improve the accuracy and stability of indoor positioning,this paper proposes an indoor positioning method based on WiFi-PDR fusion without trace particle filter.In order to reduce the influence of indoor complex environment on WiFi positioning,the weighted path loss algorithm is used to improve WiFi positioning.To reduce the cumulative effect of pedestrian track estimation errors,the walking period is divided by setting reference values and the acceleration data is smoothed and noise-reduced to improve the accuracy of step measurement.On the basis of improving WiFi and PDR positioning,a fusion positioning method using unscented particle filter is proposed,and the particle filter is optimized for robustness and adaptive to improve its robustness.Experimental simulation results show that this method can effectively improve the accuracy and stability of indoor positioning.

Key words: Indoor positioning, Information fusion, Pedestrian Dead Reckoning, Unscented particle filter, WiFi positioning

中图分类号: 

  • TP391.9
[1] KAWAGUCHI N,YANO M,ISHIDA S,et al.Undergroundpositioning:subway information system using WiFi location technology[C]//IEEE Tenth International Conference on Mobile Data Management:Systems,Services and Middleware(MDM'09).IEEE,2009:371-372.
[2] BISWAS J,VELOSO M.WiFi localization and navigation for autonomous indoor mobile robots[C]//2010 IEEE International Conference on Robotics and Automation(ICRA).IEEE,2010:4379-4384.
[3] WOODMAN O,HARLE R.Pedestrian localisation for indoorenvironments[C]//Tenth International Conference on Ubiquitous Computing.Seoul,South Korea:ACM,2018:114-123.
[4] BAHL P,PADMANABHAN V N.RADAR:an in-building RF-based user location and tracking system[C]//Nineteenth An-nual Joint Conference of the IEEE Computer and Communications Societies(INFOCOM 2000).IEEE,2000.
[5] YOUSSEF M A,AGRAWALA A,UDAYA SHANKAR A.WLAN location determination via clustering and probability distributions[C]//First IEEE International Conference on Pervasive Computing and Communications(PerCom 2013).IEEE,2013:143-150.
[6] WEINBERG H.Using the ADXL202 in pedometer and personal navigation applications[EB/OL].(2014-10-20).https://www.analog.com.
[7] QIU C,MUTKA M W.Walk and learn:Enabling accurate indoor positioning by profiling outdoor movement on smartphones[J].Pervasive and Mobile Computing,2018,48:84-100.
[8] SHIN S H,PARK C G,KIM J W,et al.Adaptive step length estimation algorithm using low-cost MEMS inertial sensors[C]//IEEE Sensors Applications Symposium(SAS'07).IEEE.2017:1-5.
[9] KIM J W,JANG H J,HWANG D H,et al.A step,stride and heading determination for the pedestrian navigation system[J].Journal of Global Positioning Systems,2014,3(1/2):273-279.
[10] ZOU H,WANG H,XIE L,et al.An RFID indoor positioningsystem by using weighted path loss and extreme learning machine[C]//International Conference on Cyber-Physical Systems,Networks,and Applications(CPSNA).2013:66-71.
[11] ZOU H,XIE L,JIA Q S,et al.Platform and algorithm development for a rfid-based indoor positioning system[J].Unmanned Systems,2014,2:279-291.
[12] READ J,ACHUTEGUI K,MIGUEZ J.A Distributed Particle Filter for Nonlinear Tracking in Wireless Sensor Networks[J].Signal Processing,2014,98:121-134.
[13] LEPPÄKOSKI H,COLLIN J,TAKALA J.Pedestrian navigation based on inertial sensors,indoor map,and WLAN signals[J].Journal of Signal Processing Systems,2013,71:287-296.
[14] CHEN Z,ZOU H,JIANG H,et al.Fusion of WiFi,smartphone sensors and landmarks using the Kalman filter for indoor localization[J].Sensors,2019,15(1):715-732.
[15] KIM J W,HAN J J,HWANG D H,et al.A Step,Stride and Heading Determination for the Pedestrian Navigation System[J].Journal of Global Positioning Systems,2014,3(1/2):273-279.
[16] BYLEMANS I,WEYN M,KLEPAL M.Mobile phone-baseddisplacement estimation for opportunistic localisation systems[C]//International Conference on Mobile Ubiquitous Computing,Systems,Services and Technologies.2009:113-118.
[17] GUO S B,SHEN F,LI Y.Indoor positioning method based on WiFi/PDR fusion[J].Electronic Information Warfare Technology,2019,34(5):24-27.
[18] DENG Z Z.Pedestrian indoor positioning algorithm based oninertial sensor and map matching [D].Harbin:Harbin Industry University,2015.
[19] ZHOU R,YUAN X Z,HUANG Y M.WiFi-PDR fusion indoor positioning based on Kalman filter[J].Journal of University of Electronic Science and Technology of China,2016,45(3):3-5.
[20] XU W.Research and implementation of indoor positioning technology based on Android mobile phones [D].Wuhan:Central China Normal University,2014.
[21] POULOSE A,KIM J,HAN D S.Indoor Localization with Smart-phones[C]//Magnetometer Calibration in IEEE International Conference on Consumer Electronics(ICCE).2019:1-3.
[22] DI Y,GU X H,LONG F,et al.An Interactive Multi-model Target Tracking Method Based on Improved UPF for Motion Acoustic Array[J].Control and Decision,2018,33(2):249-255.
[23] HUANG X P,WANG Y,MIAO P C.Principle and Application of Target Positioning and Tracking-MATLAB Simulation [M].Beijing:Electronic Industry Publishing Society,2018.
[24] YANG Y X.Principle of Equivalence-Robust Least Squares Solution of Parametric Adjustment Model[J].Bulletin of Sur-veying and Mapping,1994,29(6):33-35.
[25] ZHOU J W.Classical error theory and robust estimation[J].Journal of Surveying and Mapping,1989,48(2):115-120.
[26] WU B,TIAN Q.Indoor moving target localization algorithmbased on improved unscented particle filter[J].Sensors and Microsystems,2021,40(3):153-156.
[27] BAHL P,PADMANABHAN V N.RADAR:an in-building RF-based user location and Tracking system[C]//Nineteenth Annual Joint Conference of the IEEE Computer and Communications Societies(INFOCOM 2000).IEEE,2000.
[28] DE MORAES L F M,NUNES B A A.Calibration-free WLAN location system based on dynamic mapping of signal strength[C]//Fourth ACM International Workshop on Mobility Mana-gement and Wireless Access.IEEE,2006:92-99.
[29] LIM H,KUNG L C,HOU J C,et al.Zero-configuration,robust indoor localization:theory and experimentation[C]//Twenty-fifth IEEE International Conference on Computer Communications(INFOCOM 2006).Barcelona,Spain:IEEE,2006:1-12.
[30] RUNTASTIC.Runtastic pedometer[EB/OL].(2014-10-20).https://www.runtastic.com/zh/apps/pedometer.
[31] KULAS L.Calibration-Free Single-Anchor Indoor LocalizationUsing an ESPAR Antenna[J].Sensors,2021,21(10):3431-3440.
[32] CHEW M T,ALAM F.Accurate Ultrasound Indoor Localiza-tion Using Spring-RelaxationTechnique[J].Electronics,2021,10(11):1290-1290.
[33] JIANG Z Q,HONG X Y.RFID indoor target location algorithm based on improved particle filter[J].Information Technology,2021,20(4):107-112.
[34] YU C B,CHENG K H.WiFi and pedestrian dead reckoningadaptive unscented Kalman filter fusion positioning algorithm[J].Science Technology and Engineering,2020,20(27):11155-11160.
[35] WANG Z Z,ZANG L G,TANG Y M,et al.UWB precise positioning adaptive unscented Kalman filter algorithm[J].Agricultural Equipment and Vehicle Engineering,2021,59(5):24-28.
[1] 邵子灏, 杨世宇, 马国杰.
室内信息服务的基础——低成本定位技术研究综述
Foundation of Indoor Information Services:A Survey of Low-cost Localization Techniques
计算机科学, 2022, 49(9): 228-235. https://doi.org/10.11896/jsjkx.210900260
[2] 唐清华, 王玫, 唐超尘, 刘鑫, 梁雯.
基于M2M相遇区的PDR室内定位方法
PDR Indoor Positioning Method Based on M2M Encounter Region
计算机科学, 2022, 49(9): 283-287. https://doi.org/10.11896/jsjkx.210800270
[3] 闫佳丹, 贾彩燕.
基于双图神经网络信息融合的文本分类方法
Text Classification Method Based on Information Fusion of Dual-graph Neural Network
计算机科学, 2022, 49(8): 230-236. https://doi.org/10.11896/jsjkx.210600042
[4] 张源, 康乐, 宫朝辉, 张志鸿.
基于Bi-LSTM的期货市场关联交易行为检测方法
Related Transaction Behavior Detection in Futures Market Based on Bi-LSTM
计算机科学, 2022, 49(7): 31-39. https://doi.org/10.11896/jsjkx.210400304
[5] 李丽, 郑嘉利, 罗文聪, 全艺璇.
基于近端策略优化的RFID室内定位算法
RFID Indoor Positioning Algorithm Based on Proximal Policy Optimization
计算机科学, 2021, 48(4): 274-281. https://doi.org/10.11896/jsjkx.200300028
[6] 刘志鑫, 张泽华, 张杰.
基于多层次多视角的图注意力Top-N推荐方法
Top-N Recommendation Method for Graph Attention Based on Multi-level and Multi-view
计算机科学, 2021, 48(4): 104-110. https://doi.org/10.11896/jsjkx.200800027
[7] 徐鹤, 吴满星, 李鹏.
基于ARIMA模型的RFID室内相对位置定位算法
RFID Indoor Relative Position Positioning Algorithm Based on ARIMA Model
计算机科学, 2020, 47(9): 252-257. https://doi.org/10.11896/jsjkx.200400038
[8] 李丽,郑嘉利,王哲,袁源,石静.
基于异步优势动作评价的RFID室内定位算法
RFID Indoor Positioning Algorithm Based on Asynchronous Advantage Actor-Critic
计算机科学, 2020, 47(2): 233-238. https://doi.org/10.11896/jsjkx.190100070
[9] 张良成, 王运锋.
动态自适应的多雷达信息加权融合方法
Dynamic Adaptive Multi-radar Tracks Weighted Fusion Method
计算机科学, 2020, 47(11A): 321-326. https://doi.org/10.11896/jsjkx.2004000145
[10] 王文博, 黄璞, 杨章静.
基于超宽带、里程计、RGB-D融合的室内定位方法
Indoor Positioning Method Based on UWB Odometer and RGB-D Fusion
计算机科学, 2020, 47(11A): 334-338. https://doi.org/10.11896/jsjkx.200200033
[11] 孙志刚, 王国涛, 蒋爱平, 高萌萌, 刘金钢.
基于信息融合技术的行车安全监测系统
Monitoring System of Traffic Safety Based on Information Fusion Technology
计算机科学, 2020, 47(11A): 642-650. https://doi.org/10.11896/jsjkx.200400133
[12] 翟书颖, 李茹, 李波, 郝少阳.
视觉群智感知应用综述
Survey on Applications of Visual Crowdsensing
计算机科学, 2019, 46(6A): 11-15.
[13] 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
RFID Indoor Positioning Algorithm Combining Grasshopper Optimization Algorithm and Extreme Learning Machine
计算机科学, 2019, 46(12): 120-125. https://doi.org/10.11896/jsjkx.181202381
[14] 张美璟, 王应明.
一种考虑等级语义关联的证据推理决策方法
Decision Making Approach Based on Evidential Reasoning Considering SemanticRelationship among Assessment Grades
计算机科学, 2018, 45(12): 166-169. https://doi.org/10.11896/j.issn.1002-137X.2018.12.026
[15] 夏俊, 刘军发, 蒋鑫龙, 陈益强.
针对设备差异性问题的增量式室内定位方法
Incremental Indoor Localization for Device Diversity Issues
计算机科学, 2018, 45(10): 69-77. https://doi.org/10.11896/j.issn.1002-137X.2018.10.014
Viewed
Full text


Abstract

Cited

  Shared   
  Discussed   
No Suggested Reading articles found!