基于贝叶斯网络的楼层定位算法

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%。

Bayesian network-based floor localization algorithm

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.