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基于自适应渐消记忆的蓝牙序列匹配定位算法 被引量:7

Adaptive Fading Memory Based Bluetooth Sequence Matching Localization Algorithm
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摘要 针对传统指纹定位算法建库耗时长和定位精度低的问题,该文提出一种基于自适应渐消记忆的蓝牙序列匹配定位算法。首先,利用行人航迹推算(PDR)和最近邻算法(NNA)对运动序列进行位置标定和接收信号强度(RSS)映射;然后,根据邻近位置的相关性,采用序列递归搜索算法构建指纹序列数据库;最后,通过自适应渐消记忆算法,并结合初始序列匹配度实现位置估计。实验结果表明,该算法在室内环境下能够获得较低的建库时间开销以及较高的定位精度。 The traditional fingerprinting localization algorithm has high construct time overhead and low positioning accuracy. Because of this problem, an adaptive fading memory based bluetooth sequence matching localization algorithm is proposed. Firsly, Pedestrian Dead Reckoning(PDR) and Nearest Neighbor Algorithm(NNA) are applied to performing position calibration and Received Signal Strength(RSS) mapping of Motion Sequences. Secoudly, according to the relevance of neighboring locations, a sequence recursive search method is used to construct fingerprint sequence database. Finally, an adaptive fading memory algorithm and initial sequence matching degree are considered to realize the position estimation of target. The experimental results show that this algorithm is able to consume low construct time overhead and achieve high indoor localization precision.
作者 田增山 王阳 周牧 未平 TIAN Zengshan;WANG Yang;ZHOU Mu;WEI Ping(School of Communication and Information Engineering, Chongqing University of Posts andTelecommunications, Chongqing 400065, China)
出处 《电子与信息学报》 EI CSCD 北大核心 2019年第6期1381-1388,共8页 Journal of Electronics & Information Technology
基金 国家自然科学基金(61771083,61704015) 长江学者和创新团队发展计划基金(IRT1299) 重庆市科委重点实验室专项经费,重庆市基础与前沿研究计划基金(cstc2017jcyjAX0380,cstc2015jcyjBX0065) 重庆市高校优秀成果转化基金(KJZH17117) 重庆市研究生科研创新项目(CYS17221) 重庆市教委科学技术研究项目(KJ1704083)~~
关键词 室内定位 低功耗蓝牙 行人航迹推算 序列递归搜索 自适应渐消记忆 Indoor localization Bluetooth Low Energy(BLE) Pedestrian Dead Reckoning(PDR) Sequence recursive search Adaptive fading memory
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  • 1茹滨超,鲜斌,宋英麟,曹美会.基于气压传感器的无人机高度测量系统[J].中南大学学报(自然科学版),2013,44(S2):94-97. 被引量:22
  • 2何友,关欣,王国宏.多传感器信息融合研究进展与展望[J].宇航学报,2005,26(4):524-530. 被引量:61
  • 3李小文,曹春香,常超一.地理学第一定律与时空邻近度的提出[J].自然杂志,2007,29(2):69-71. 被引量:115
  • 4蒋恩松,李孟超,孙刘杰.一种基于神经网络的卡尔曼滤波改进方法[J].电子与信息学报,2007,29(9):2073-2076. 被引量:19
  • 5Moghtadaiee V and Dempster A. WiFi fingerprinting signalstrength error modeling for short distances[C]. Proceedings ofthe International Conference on Indoor Positioning andIndoor Navigation (IPIN), Sydney, Australia, 2012: 13-15.
  • 6Yu Y, Yang J, McKelvey T, et al. A compact UWB indoorand through-wall radar with precise ranging and tracking[J],International Journal of Antennas and Propagation, 2012,25(6): 38-46.
  • 7Foxlin E. Pedestrian tracking with shoe-mounted inertialsensors [J]. Computer Graphics and Applications, 2005,30(5):20-26.
  • 8Nilsson J, Skog I, and Handel P. A note on the limitations ofZUPTs and the implications on sensor error modeling[C].Proceedings of the International Conference on IndoorPositioning and Indoor Navigation (IPIN), Sydney, Australia,2012: 20-22.
  • 9Borenstein J and Ojeda L. Heuristic drift elimination forpersonnel tracking systems[J]. Journal of Navigation, 2010,63(4): 591-606.
  • 10Angermann M and Robertson P. Footslam: Pedestriansimultaneous localization and mapping without exteroceptivesensors --hitchhiking on human perception and cognition[J].Proceedings of the IEEE, 2012, 100(Special Centennial Issue):1840-1848.

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