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计算机工程 ›› 2019, Vol. 45 ›› Issue (4): 66-71,77. doi: 10.19678/j.issn.1000-3428.0048728

• 移动互联与通信技术 • 上一篇    下一篇

基于扩展Kalman滤波的室内WiFi-PDR融合定位算法

刘庆1,关维国1,李顺康1,王芳2   

  1. 1.辽宁工业大学 电子与信息工程学院,辽宁 锦州121001; 2.国网辽宁省电力有限公司锦州供电公司,辽宁 锦州 121000
  • 收稿日期:2017-09-19 出版日期:2019-04-15 发布日期:2019-04-15
  • 作者简介:刘庆(1988—),男,硕士研究生,主研方向为移动通信、无线技术;关维国(通信作者),教授、博士;李顺康,硕士研究生;王芳,高级工程师。
  • 基金资助:

    辽宁省自然科学基金“基于北斗与泛在无线网络的室内外协同定位技术研究”(20170540437);辽宁省教育厅重大科技平台科技项目(JP2016015)。

Indoor WiFi-PDR Fusion Location Algorithm Based on Extended Kalman Filter

LIU Qing 1,GUAN Weiguo 1,LI Shunkang 1,WANG Fang 2   

  1. 1.College of Electronic and Information Engineering,Liaoning University of Technology,Jinzhou,Liaoning 121001,China; 2.State Grid Liaoning Electric Power Limited Company Jinzhou Power Supply Company,Jinzhou,Liaoning 121000,China
  • Received:2017-09-19 Online:2019-04-15 Published:2019-04-15

摘要:

为解决室内WiFi定位精度较低及行人航位推算(PDR)定位存在累积误差的问题,提出一种基于扩展Kalman滤波(EKF)的WiFi-PDR融合定位算法。WiFi通过改进的WKNN算法实现匹配定位,根据定位点与K近邻点的接收信号强度指示相对偏差进行权值修正,PDR定位采用多重约束条件的步态检测和在线步长估计方法。在此基础上,将EKF作为WiFi和PDR定位的融合滤波器,以降低WiFi定位回跳和PDR累计误差,从而提高定位精度。实验结果表明,在多次行迹转弯条件下,该融合定位算法的定位精度可达1.8 m。

关键词: 室内定位, WiFi定位, 行人航位推算, 扩展Kalman滤波, 融合定位

Abstract:

Indoor WiFi location accuracy is low,and Pedestrian Dead Reckoning(PDR) location has the problem of cumulative error.Therefore,a WiFi-PDR fusion location algorithm based on Extended Kalman Filter(EKF) is proposed.WiFi achieves matching location by improved WKNN algorithm,and the relative deviation of the Received Signal Strength Indication(RSSI) between the location point and the K-nearest neighbor point is used for weight correction.PDR location adopts gait detection with multiple constraints and online step size estimation.On this basis,EKF is used as the fusion filter of WiFi and PDR location to reduce the WiFi location bounce and the cumulative error of PDR to improve the location accuracy.Experimental results show that the location accuracy of the fusion algorithm can reach 1.8 m under the condition of multiple traveling turns.

Key words: indoor location, WiFi location, Pedestrian Dead Reckoning(PDR), Extended Kalman Filter(EKF), fusion location

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