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%。
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 1 247 times floor changing experiments.
作者
张榜
朱金鑫
徐正蓺
刘盼
魏建明
ZHANG Bang;ZHU Jinxin;XU Zhengyi;LIU Pan;WEI Jianming(Shanghai Advanced Research Institute,Chinese Academy of Sciences,Shanghai 201210,China;University of Chinese Academy of Sciences,Beijing 100049,China;School of Communication and Information Engineering,Shanghai University,Shanghai 200444,China)
出处
《计算机应用》
CSCD
北大核心
2019年第8期2468-2474,共7页
journal of Computer Applications
基金
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)~~
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
indoor positioning
floor localization
Bayesian network
Extended Kalman Filter (EKF)
corner detection
