基于RSSI与惯性测量的室内定位系统

doi:10.3969/j.issn.1000-3428.2013.11.020

计算机工程

基于RSSI与惯性测量的室内定位系统

胡伟娅，陆佳亮，伍民友

(上海交通大学计算机科学与工程系，上海 200240)

收稿日期:2012-10-26 出版日期:2013-11-15 发布日期:2013-11-13
作者简介:胡伟娅(1988－)，女，硕士，主研方向：无线传感器网络；陆佳亮，讲师、博士；伍民友，教授、博士生导师、CCF高级会员
基金资助:
国家自然科学基金资助项目(61100210)

Indoor Positioning System Based on RSSI and Inertial Measurement

HU Wei-ya, LU Jia-liang, WU Min-you

(Department of Computer Science and Engineering, Shanghai Jiaotong University, Shanghai 200240, China)

Received:2012-10-26 Online:2013-11-15 Published:2013-11-13

摘要/Abstract

摘要： 介绍融合接收信号强度指示(RSSI)和惯性测量技术的无线传感器室内定位系统，该系统通过可穿戴式无线传感器节点和环境辅助传感器节点，采集步行者的位置信息。可穿戴式节点采用Dead Reckoning惯性测量方法，存在累积误差，可通过在室内环境中布置RSSI节点矫正步行者的位置信息。采用扩展性的卡尔曼滤波算法将惯性测量与RSSI测量数据相结合，实现自适应的步长算法，较大程度改进步长不正确读取带来的误差。实验结果表明，与纯粹的惯性测量系统相比，该系统能提高66.3%的精确度。

关键词: 接收信号强度指示, 惯性测量, 无线传感器, 扩展性的卡尔曼滤波, 自适应步长

Abstract: This paper introduces a wireless sensor tracking system for personal indoor positioning based on Received Signal Strength Indicator(RSSI) and Dead Reckoning(DR) technology. The system is built with portable on-body sensor nodes and assisted sensor nodes deployed in the targeted indoor area. It takes a hybrid approach with pedestrian dead reckoning and radio-based localization. Real-time inertial measurements are combined with RSSI-based information, and processed with an extended Kalman filtering to be weighted in the location estimation according to their reliability. It incorporates with an adaptive step length algorithm to reduce the deviation of the measurements. Experimental results show that the system can improve the accuracy of the positioning by 66.3% compared with pure inertial dead reckoning system.

Key words: Received Signal Strength Indicator(RSSI), Inertial Measurement, wireless sensor, extended Kalman filtering, adaptive step length

中图分类号:

TP391

胡伟娅，陆佳亮，伍民友. 基于RSSI与惯性测量的室内定位系统[J]. 计算机工程, doi: 10.3969/j.issn.1000-3428.2013.11.020.

HU Wei-ya, LU Jia-liang, WU Min-you. Indoor Positioning System Based on RSSI and Inertial Measurement[J]. Computer Engineering, doi: 10.3969/j.issn.1000-3428.2013.11.020.

http://www.ecice06.com/CN/Y2013/V39/I11/91

参考文献

参考文献 [1] Thurston J. GALILEO, GLONASS and NAVSTAR A Report on GPS for GIS People[C]//Proc. of GISCafe’02. [S. 1.]: IEEE Press, 2002. [2] Gu Y, Lo A, Niemegeers I. A Survey of Indoor Positioning Systems for Wireless Personal Networks[J]. IEEE Com- munications Surveys & Tutorials, 2009, 11(1): 13-32. [3] Bekkali A. RFID Indoor Positioning Based on Probabilistic RFID Map and Kalman Filtering[C]//Proc. of WiMOB’07. [S. 1.]: IEEE Press, 2007. [4] Bahl P, Padmanabhan V. RADAR: an In-building RF-based User Location and Tracking System[C]//Proc. of IEEE INFOCOM’00. Tel Aviv, Israel: IEEE Press, 2000: 775-784. [5] King T, Kopf S, Haenselmann T. COMPASS: A Probabilistic Indoor Positioning System Based on 802.11 and Digital Compasses[C]//Proc. of the 1st ACM Int’l Workshop on WiNTECH’06. Los Angeles, USA: [s. n.], 2006. [6] Collin J, Mezentsev O, Lachapelle G, et al. Indoor Positioning System Using Accelerometry and High Accuracy Heading Sensors[C]//Proc. of GPS/GNSS Conference. Portland, USA: [s. n.], 2003. [7] Cliff C, Muller H. Personal Position Measurement Using Dead Reckoning[C]//Proc. of the 7th Int’l Symposium on Wearable Computers. New York, USA: [s. n.], 2003: 166-173. [8] Beauregard S, Haas H. Pedestrian Dead Reckoning: A Basis for Personal Positioning[C]//Proc. of the 3rd Workshop on Positioning, Navigation and Communication. Hannover, Germany: [s. n.], 2006: 27-35. [9] Jin Y, Motani M, Soh W, et al. SparseTrack: Enhancing Indoor Pedestrian Tracking with Sparse Infrastructure Support[C]// Proc. of IEEE INFOCOM’10. San Diego, USA: [s. n.], 2010: 1-9. [10] Sparkfun 9 dof Razor IMU[EB/OL]. (2012-10-20). http://www. sparkfun.com. [11] Xbee 2 802.15.4 OEM rf Module[EB/OL]. (2012-10-20). http://www.digi.com/. [12] Leppakoski H, Kappi J, Syrjarinne J, et al. Error Analysis of Step Length Estimation in Pedestrian Dead Reckoning[C]// Proc. of IONGPS’02. Portland, USA: [s. n.], 2002: 1136-1142. [13] Shin S, Park C, Kim J, et al. Adaptive Step Length Estimation Algorithm Using Low-cost MEMS Inertial Sensors[C]//Proc. of IEEE Sensors Applications Symposium. San Diego, USA: [s. n.], 2007: 1-5. 编辑索书志

[1]	杨敏, 张玲华. WSN中基于区域感知的改进AODV路由协议[J]. 计算机工程, 2023, 49(8): 130-136.
[2]	区展华, 李翠然, 杨茜. 基于ANN的能量采集无线传感器网络中继选择策略[J]. 计算机工程, 2023, 49(5): 215-222,230.
[3]	彭虎, 李源汉, 邓长寿, 吴志健. 多策略调和的布谷鸟搜索算法[J]. 计算机工程, 2022, 48(8): 85-97.
[4]	周文康, 王行甫. 一种基于AHP和FIS的WSN路由算法[J]. 计算机工程, 2022, 48(3): 131-138,161.
[5]	王海浪, 张玲华. 基于PEGASIS的无线传感器网络路由协议改进[J]. 计算机工程, 2022, 48(12): 165-171,179.
[6]	张清国, 张勇, 张伟, 席瑞洁. 基于蜂窝结构的改进混合无线传感器网络覆盖优化算法[J]. 计算机工程, 2022, 48(12): 172-179.
[7]	缪欣, 陈璇, 鲍红莹, 张静轩, 余炜. 移动传感器网络中路径扫描覆盖问题研究[J]. 计算机工程, 2022, 48(12): 150-155,164.
[8]	缪祎晟, 赵春江, 吴华瑞. 基于改进粒子群的农田WSN路由优化方法[J]. 计算机工程, 2022, 48(10): 218-223.
[9]	陈辉, 高岩. 基于双簇头的WSNs非均匀分簇路由算法[J]. 计算机工程, 2022, 48(10): 184-192.
[10]	韩优佳, 胡黄水, 姚美琴. 基于信任感知的无线传感器网络安全路由协议[J]. 计算机工程, 2021, 47(9): 145-152.
[11]	于吉喆, 白乐强, 曹科研. 基于垂线的WSN基站位置隐私保护算法[J]. 计算机工程, 2021, 47(8): 170-176,182.
[12]	蒋宝庆, 陈宏滨. 基于Q学习的无人机辅助WSN数据采集轨迹规划[J]. 计算机工程, 2021, 47(4): 127-134,165.
[13]	胡乔木, 邓昀. 基于压缩HMAC算法的传感器网络范围查询方法[J]. 计算机工程, 2021, 47(12): 200-208.
[14]	李翠然, 李昂. 太阳能补给的线型WSN能量高效路由算法[J]. 计算机工程, 2020, 46(9): 178-185.
[15]	柳亚男, 张正, 邱硕, 程远. WSN中基于物理不可克隆函数的簇内密钥分配[J]. 计算机工程, 2020, 46(9): 163-171.

选择文件类型/文献管理软件名称

选择包含的内容

基于RSSI与惯性测量的室内定位系统

Indoor Positioning System Based on RSSI and Inertial Measurement

PDF

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 15

编辑推荐

Metrics

本文评价

模态框（Modal）标题

选择文件类型/文献管理软件名称

选择包含的内容

基于RSSI与惯性测量的室内定位系统

Indoor Positioning System Based on RSSI and Inertial Measurement

PDF

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 15

编辑推荐

Metrics

本文评价