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基于空间分集和轨迹连续的实时Fingerprint定位算法 被引量：1

Real time fingerprint positioning algorithm based on spatial diversity and trajectory continuity
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摘要 在室外复杂环境下,接收信号强度的浮动是制约Fingerprint定位精度的一个主要问题。传统的方法利用时间分集和平均滤波器来减小信号的浮动,这增加了定位的延迟,无法满足室外实时定位的需求。为了解决信号浮动和定位延迟问题,在分析接收信号强度空间相关性的基础上,提出了一种实时的Fingerprint定位算法,利用空间分集减小信号的浮动和定位延迟,同时根据运动物体轨迹的连续性提高定位精度。室外环境下的实验结果表明了所提算法能显著提高Fingerprint定位系统的性能。 Received signal strength fluctuations are a major limit to positioning accuracy in complex outdoor environments.Conventional approaches use the temporal diversity and an average filter to reduce the signal strength fluctuations,which increases the latency in estimating the location so they cannot satisfy the demands of outdoor real-time positioning systems.A real-time algorithm for fingerprint positioning was developed based on an analysis of the spatial correlation of the received signal strength.The algorithm uses the spatial diversity to reduce the signal strength fluctuations and the positioning latency.The algorithm then uses the continuity of the movement trajectory to improve the positioning accuracy.Tests in outdoor environments indicate that the algorithm significantly improves fingerprint positioning performance.
作者 刘兴川 张盛 徐立强 林孝康
机构地区 清华大学深圳研究生院
出处 《清华大学学报（自然科学版）》 EI CAS CSCD 北大核心 2011年第2期176-179,共4页 Journal of Tsinghua University(Science and Technology)
基金 清华-意法半导体联合研究项目资助课题
关键词 信号强度 Fingerprint定位 空间分集 轨迹连续性 均方根误差(RMSE) signal strength fingerprint positioning spatial diversity trajectory continuity root mean square error（RMSE）
分类号 TN929.5 [电子电信—通信与信息系统]
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参考文献12

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同被引文献14

  • 1Hightower J, 13orriello G. Location systems for ubiquitous com- puting[J]. IEEE Computer Magazine, 2001, 34(8) :57 - 66.
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  • 3Chon Y, Cha H. Lifemap: a smartphone-based context provider for location-based service [ J ]. IEEE I2erzuasive Computing Magazine, 201l, 10(2):58-67.
  • 4Miluzzo E, Nicholas D, Krist6f F, et al. Sensing meets mobile social networks., the design, implementation, and evaluation of the cenceme application[C]//Proc, of the 6th Association .for Computing Machinery Conference Embedded Network Sensor Systems, 2008:337 - 350.
  • 5Brunato M, BattitiR. Statistical learning theory for location fin gerprinting in wireless LANs[J]. Computer Networks, 2005, 47(6): 825-845.
  • 6Roos T, Myllymaki P, Tirri H, et al. A probabilistic approach to WLAN user location estimation[J].International Journal Wireless Information Networks, 2002, 9(3):155- 164.
  • 7Liu H, Darabi H, Banerjee P, et al. Survey of wireless indoor positioning techniques and systems[J]. IEEE Trans. on Sys tems, 2007, 37(6):1067-1080.
  • 8Fang S, Lin T, Lee IK. A novel algorithm for multipath finger prinling in indoor WLAN environments[J].IEEE Trans. on Wireless Communication, 2008, 7(9): 3579-3588.
  • 9Eddie C, Chan G, Mark S. Using Wi Fi signal strength to local ize in wireless sensor networks[C]//Proc, of the International Conference on Communications and Mobile Computing, 2009: 538 - 542.
  • 10Zirari S, Canalda P. WiFi GPS based combined positioning al gorithm[C]//Proc, of the Wireless Communications, Networ- king and Information Security, 2010 : 684 - 688.

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清华大学学报（自然科学版）
2011年 第2期

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