期刊文献+

基于蓝牙和PDR结合的室内定位方法研究 被引量:2

Research on indoor location method based on Bluetooth and PDR
下载PDF
导出
摘要 随着中国城市化进程的发展,室内定位受到广泛关注。为解决单一室内定位误差大、WiFi组合定位成本高等问题,文章提出了低功耗蓝牙(iBeacon)和行人航位推算(PDR)相结合的室内组合定位方案。该方案先利用iBeacon定位原理及改进的K近邻算法对待定位终端进行实时定位,同时利用PDR定位估算的坐标减小iBeacon指纹定位算法搜索指纹库的时间,减小定位系统的时间复杂度和空间复杂度,并利用融合算法启动测量更新和状态更新的迭代计算,更新算法中的参数,实现组合定位。 With the development of China's urbanization, indoor location attracts increasingly considerable attention, which suggests a positioning solution combining i Beacon and PDR, aimed at disposing of unacceptable deviation of indoor location alone, high cost of the WiFi combined positioning and so on. This project makes use of iBeacon positioning principle and revised K-Nearest Neighbor to locate the terminals in real time, estimated coordinates with PDR positioning to reduce the time searching fingerprint library by iBeacon fingerprint location algorithm and the temporal and dimensional positioning complexity, fusion algorithm to start iterative computation of measurement and status update to update the parameters in step size estimation algorithm, which brings about integrated positioning.
作者 赵菲 Zhao Fei(Xi’an University of Architecture and Technology,Xi’an 710055,China)
出处 《无线互联科技》 2018年第16期17-19,共3页 Wireless Internet Technology
关键词 室内组合定位 K邻近算法 融合算法 无迹卡尔曼滤波 indoor combined positioning K-nearest neighbor fusion algorithm unscented Kalman flter
  • 相关文献

参考文献6

二级参考文献37

  • 1王睿,赵方,彭金华,罗海勇,陆波,陆涛.基于WI-FI和蓝牙融合的室内定位算法[J].计算机研究与发展,2011,48(S2):28-33. 被引量:31
  • 2于秀芬,段海滨,龚华军.移动机器人视觉定位方法的研究与实现[J].数据采集与处理,2004,19(4):433-437. 被引量:7
  • 3HARLE R. A Survey of Indoor Inertial Positioning Sys- terns for Pedestrians [ J ]. IEEE Transactions on Commu- nications Surveys & Tutorials. 2013, 15 ( 3 ) : 1281- 1293.
  • 4IBARRA B, RAMIREZ C. Pedestrian Dead Reckoning towards Indoor Location Based Applications [ C ]. Interna- tional Conference on Electrical Engineering Computing Science and Automatic Control. 2011, 1 (6) : 26-28.
  • 5LI W L, ILTIS. A Smartphone Localization Algorithm using RSSI and Inertial Sensor Measurement Fusion [ C ]. IEEE Global Communications Conference. 2013, 9 (13) : 3335 -3340.
  • 6YUNG F H, et al. Performance of an MMSE Based In- door Localization with Wireless Sensor Networks[ C]. In- ternational Conference on Networked Computing and Ad- vaneed Information Management, 2010.
  • 7HARA S, ANZAI D. Use of a Simplified Maximum Like- lihood Function in a WLAN-Based Location Estimation [ C ]. IEEE Conference on Wireless Communications and Networking, 2009.
  • 8XIANGLING Z, CHANGXU W. Modeling Pedestrian Crossing Paths at Unmarked Roadways [ J ]. IEEE Trans- actions on. Intelligent Transportation Systems. 2013, 1438-1448.
  • 9ROBERTSON P, ANGERMANN M, KRACH B. Simul- taneous Localization and Mapping for Pedestrians using Only Footmounted Inertial Sensors [ C ]. 11 th Internation- al conference on Ubiquitous computing, 2009.
  • 10JIMENEZ A R, SECO F, PRIETO C, et al. A Compari- son of Pedestrian Dead-Reckoning Algorithms using a Low-cost MEMS IMU [ C ]. IEEE International conference on Intelligent Signal Processing Symposium. 2009:37-42.

共引文献71

同被引文献4

引证文献2

二级引证文献4

相关作者

内容加载中请稍等...

相关机构

内容加载中请稍等...

相关主题

内容加载中请稍等...

浏览历史

内容加载中请稍等...
;
使用帮助 返回顶部