东北大学学报（自然科学版）

东北大学学报:自然科学版 ›› 2014, Vol. 35 ›› Issue (10): 1390-1393.DOI: 10.12068/j.issn.1005-3026.2014.10.006

• 信息与控制 • 上一篇    下一篇

基于滑动窗口最长公共子序列WiFi指纹定位算法

张明洋1，陈剑1,2，闻英友1,2，赵宏1,2   

  1. (1东北大学 信息科学与工程学院， 辽宁 沈阳110819； 2东北大学 医学影像计算教育部重点实验室， 辽宁 沈阳110819)
  • 收稿日期:2014-04-23 修回日期:2014-04-23 出版日期:2014-10-15 发布日期:2014-05-19
  • 通讯作者: 张明洋
  • 作者简介:张明洋(1989-)，男，辽宁辽阳人，东北大学博士研究生；赵宏(1954-)，男，河北河间人，东北大学教授.
  • 基金资助:
    国家自然科学基金资助项目(60903159，61173153)；沈阳市科技计划项目(1091176-1-00)；中央高校基本科研业务费专项资金资助项目(N110318001，N100218001).

WiFi Fingerprint Localization Algorithm Based on Sliding Window Combined with Longest Common Subsequence

ZHANG Mingyang1， CHEN Jian1,2， WEN Yingyou1,2， ZHAO Hong1,2   

  1. 1. School of Information Science & Engineering， Northeastern University， Shenyang 110819， China； 2. Key Laboratory of Medical Image Computing of Ministry of Education， Northeastern University， Shenyang 110819， China.
  • Received:2014-04-23 Revised:2014-04-23 Online:2014-10-15 Published:2014-05-19
  • Contact: ZHANG Mingyang
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摘要： 针对基于WiFi瞬时指纹定位算法中由于RSS信号的时变特性引起的WiFi定位精度差问题，提出了一种基于滑动窗口最长公共子序列指纹定位算法.该算法将时间序列的RSS信号指纹转化为基于滑动窗口的数据模型，增加了指纹特征信息，提高比对准确性.通过计算请求定位数据与样本的最长公共子序列来获得样本点的相似性，解决由于窗口伸缩或滑动窗口中个别采样点无信号引起的比对不准确问题，从而提高了定位的精确性和鲁棒性.实验结果表明，所提定位算法的结果明显优于瞬时指纹定位算法.

关键词: 室内定位, 指纹, 滑动窗口, 时间序列, 最长公共子序列

Abstract: To reduce the negative effect in the WiFi fingerprint localization algorithm caused by the fluctuation of the received signal strength (RSS)， a WiFi fingerprint localization algorithm based on sliding window combined with the longest common subsequence was proposed. First， the time sequence RSS fingerprints were converted to the sliding window data model to increase the fingerprint characteristic information and improve the matching accuracy. And then， the requesting location data and the longest common subsequence were calculated to get the similarity of sampling points， which could solve the problem caused by the window scaling or the individual sampling point without signal in the sliding window， thereby the localization accuracy and robustness were improved. The results showed that the proposed localization algorithm was superior to the instantaneous fingerprints localization algorithm.

Key words: indoor localization, fingerprint, sliding window, time series, longest common subsequence

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