计算机应用 ›› 2019, Vol. 39 ›› Issue (8): 2468-2474.DOI: 10.11772/j.issn.1001-9081.2019010119

• 应用前沿、交叉与综合 • 上一篇    下一篇

基于贝叶斯网络的楼层定位算法

张榜1,2, 朱金鑫1,3, 徐正蓺1,2, 刘盼1,2, 魏建明1   

  1. 1. 中国科学院 上海高等研究院, 上海 201210;
    2. 中国科学院大学, 北京 100049;
    3. 上海大学 通信与信息工程学院, 上海 200444
  • 收稿日期:2019-01-17 修回日期:2019-03-13 出版日期:2019-08-10 发布日期:2019-04-15
  • 通讯作者: 徐正蓺
  • 作者简介:张榜(1993-),男,福建莆田人,硕士研究生,主要研究方向:惯性传感器应用、机器学习;朱金鑫(1993-),女,黑龙江哈尔滨人,硕士,主要研究方向:惯性传感器应用、模式识别;徐正蓺(1987-),男,上海人,博士,主要研究方向:无线传感网、数据融合;刘盼(1993-),男,河北保定人,硕士研究生,主要研究方向:惯性传感器应用、机器学习;魏建明(1973-),男,湖南永州人,研究员,博士,主要研究方向:城市公共安全、先进监测与预警。
  • 基金资助:
    国家重点研发计划项目(2016YFC0801505);上海市青年科技英才扬帆计划项目(18YF1425600)。

Bayesian network-based floor localization algorithm

ZHANG Bang1,2, ZHU Jinxin1,3, XU Zhengyi1,2, LIU Pan1,2, WEI Jianming1   

  1. 1. Shanghai Advanced Research Institute, Chinese Academy of Sciences, Shanghai 201210, China;
    2. University of Chinese Academy of Sciences, Beijing 100049, China;
    3. School of Communication and Information Engineering, Shanghai University, Shanghai 200444, China
  • Received:2019-01-17 Revised:2019-03-13 Online:2019-08-10 Published:2019-04-15
  • Supported by:
    This work is partially supported by the National Key Research and Development Project (2016YFC0801505), the Shanghai Sailing Program (18YF1425600).

摘要: 针对在室内定位导航过程中单独依赖行人高度位移推测楼层位置误差较大的问题,提出一种基于贝叶斯网络的楼层定位算法。该算法先是利用扩展卡尔曼滤波(EKF)对惯性传感器数据和气压计数据进行融合,计算出行人垂直位移;然后利用误差补偿后的加速度积分特征对行人在楼梯中的转角进行检测;最后,利用贝叶斯网络融合行人行走高度和转角信息推测行人在某一层的概率,从而将行人定位在建筑物中最可能出现的楼层上。实验结果表明,与基于高度的楼层定位算法相比,所提算法的楼层定位准确率提升6.81%;与平台检测算法相比,该算法的楼层定位准确率提升14.51%;所提算法在总共1247次楼层变换实验中,楼层定位准确率达到99.36%。

关键词: 室内定位, 楼层定位, 贝叶斯网络, 扩展卡尔曼滤波, 转角检测

Abstract: In the process of indoor positioning and navigation, a Bayesian network-based floor localization algorithm was proposed for the problem of large error of floor localization when only the pedestrian height displacement considered. Firstly, Extended Kalman Filter (EKF) was adopted to calculate the vertical displacement of the pedestrian by fusing inertial sensor data and barometer data. Then, the acceleration integral features after error compensation was used to detect the corner when the pedestrian went upstairs or downstairs. Finally, Bayesian network was introduced to locate the pedestrian on the most likely floor based on the fusion of walking height and corner information. Experimental results show that, compared with the floor localization algorithm based on height displacement, the proposed algorithm has improved the accuracy of floor localization by 6.81%; and compared with the detection algorithm based on platform, the proposed algorithm has improved the accuracy of floor localization by 14.51%. In addition, the proposed algorithm achieves the accuracy of floor localization by 99.36% in the total 1247 times floor changing experiments.

Key words: indoor positioning, floor localization, Bayesian network, Extended Kalman Filter (EKF), corner detection

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