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基于粒子滤波的PDR定位算法 被引量：8

Localization algorithm of PDR based on PF
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摘要 针对目前行人航迹推算(PDR)定位精度不高的缺点,提出了一种基于粒子滤波(PF)的PDR定位算法。使用手机内置的加速度计进行步伐检测和计步,陀螺仪和磁力计进行方向估计,结合地图匹配信息,使用粒子滤波算法对定位数据进行滤波融合。实验结果表明:提出的算法提高了定位精度。 Aiming at shortcomings of low localization precision of pedestrian dead reckoning （ PDR）, a PDR algorithm based on particle filtering（PF） is proposed. The positioning algorithm uses the built-in accelerometer to detect and count the steps, and gyroscope and magnetometer are used for direction estimation. According to information of map matching,PF algorithm is used for data filtering fusion. The experimental results show that the PDR positioning algorithm based on PF improves positioning precision.
作者 钟亚洲 吴飞 任师涛 ZHONG Ya-zhou;WU Fei;REN Shi-tao(School of Electronic and Electrical Engineering,Shanghai University of Engineering Science,Shanghai 201620,China)
机构地区 上海工程技术大学电子电气工程学院
出处 《传感器与微系统》 CSCD 2018年第8期147-149,153,共4页 Transducer and Microsystem Technologies
基金 国家自然科学基金资助项目(61272097) 上海市科技学术委员会资助项目(13510501400) 上海市自然科学基金资助项目(17ZR1411900) 上海市信息安全综合管理技术研究重点实验室项目(AGK2015006) 上海高校青年教师培养计划资助项目(ZZGCD15090) 上海工程技术大学科研启动项目(2016-56)
关键词 室内定位 行人航迹推算 粒子滤波 地图匹配 indoor localization pedestrian dead reckoning （PDR） particle filtering（PF） map matching
分类号 TP393 [自动化与计算机技术—计算机应用技术]
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  • 1Foxlin E. Pedestrian tracking with shoe-mounted inertial sensors[J]. IEEE Computer Graphics and Applications, 2005, 25 (6): 38-46.
  • 2Abdulrahim K, Hide C, Moore T, et al. Aiding MEMS IMU with building heading for indoor pedestrian navigation[C]// Ubiquitous Positioning Indoor and Location Based Service (UPINLBS 2010). Krikkonummi, Finland, October 14-15, 2010.
  • 3Jimenez A R, Seco F, Prieto J C, et al. Indoor pedestrian navigation using an INS/EKF framework for yaw drift reduction and a foot-mounted IMU[C]// The 7th Workshop on Positioning, Navigation and Communication(WPNC 2010). Dresden, Germany, March 11-12, 2010.
  • 4Simon D. Optimal state estimation[M]. New Jersey: John Wiley & Sons, Inc., 2006.
  • 5Arulampalam S, Maskell S, Gordon N, et al. A tutorial on particle filters for online nonlinear/non-Gaussian Bayesian tracking[J]. IEEE Transaction on Signal Processing, 2002, 50(2): 174-188.
  • 6Krach B, Robertson P. Integration of foot-mounted inertial sensors into a Bayesian location estimation framework[C]// The 5th Workshop on Positioning, Navigation and Communication (WPNC 2008). Hannover, Germany, March 27, 2008.
  • 7Woodman O, Harle R. Pedestrian localization for indoor environments[C]//UbiComp'08. Seoul, Korea September 21-24, 2008.
  • 8Widyawan, Klepal M, Beauregard S. A backtracking particle filter for fusing building plans with PDR displacement estimates[C]// The 5th Workshop on Positioning, Navigation and Communication(WPNC 2008). Hannover, Germany, March 27, 2008.
  • 9Savvides A, Han CC, Strivastava MB. Dynamic fine-grained localization in ad-hoc networks of sensors. In: Proc. of the ACM Int'l Conf. on Mobile Computing and Networking (MOBICOM). 2001. 166-179. [doi: 10.1145/381677.381693].
  • 10McGuire M, Plataniotis KN, Venetsanopoulos AN. Location of mobile terminals using time measurements and survey points. IEEE Trans. on Vehicular Technology, 2003,52(4):999-1011. [doi: 10.1109/TVT.2003.814222].

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传感器与微系统
2018年 第8期

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