融合自适应步长与地图匹配的智能手机PDR室内定位算法

PDR indoor positioning algorithm for smartphonesintegrated adaptive step size and map matching

针对经典的PDR算法中步长估算模型无法适应不同个体之间差异以及手机内部传感器航向角估算不准确的问题,提出了一种融合自适应步长与地图匹配的智能手机PDR室内定位算法。该算法在Weinberg模型的基础上,引入相邻波峰时间差函数以提高步长模型的适应性。采用双阈值法进行步频检测,并利用卡尔曼滤波优化地图匹配后的航向角,最终实现室内行人的精确定位。实验数据显示,所提算法总路程的闭环误差为0.58%,与仅使用自适应步长相比下降了73.29%,与仅使用地图匹配相比下降了59.21%,行程72 m的累积误差为4.484 m,相较于经典的PDR算法下降了28.21%。改进后的算法定位精度显著提高,且闭环误差和累积误差更小。实验结果表明,该算法具有良好的工程应用价值。

A smart phone PDR indoor positioning algorithm that integrates adaptive step size and map matching is proposed to address the problems of the step size estimation model in the classic PDR algorithm being unable to adapt to the differences between different individuals and the inaccurate estimation of the heading angle of internal sensors in mobile phones.This algorithm is based on the Weinberg model and introduces the adjacent peak time difference function to improve the adaptability of the step size model.The dual threshold method is used for step frequency detection,and the Kalman filter is used to optimize the heading angle after map matching,ultimately achieving accurate indoor pedestrian positioning.The experimental data shows that the closed-loop error of the proposed algorithm for the total distance is 0.58%,which is 73.29%lower than using only adaptive step length and 59.21%lower than using only map matching.The cumulative error for a 72-meter journey is 4.848 meters,which is 28.21%lower than the classical PDR algorithm.The improved algorithm significantly improves positioning accuracy,and the closed-loop error and cumulative error are smaller.The experimental results indicate that the algorithm has good engineering application value.