基于多模型融合的室内行人航迹推算建模与性能分析

Modeling and performance analysis of indoor PDR based on multi-model fusion

针对行人航位推算(pedestrian dead reckoning,PDR)室内信号易受到环境和多径效应干扰的问题,提出一种基于多模型融合的室内PDR优化建模方法.给出多模型融合的室内PDR建模方法系统模型,包括步数检测、步长推算、方向推算以及位置推算4个关键阶段.该方法在步数检测阶段融合了峰值检测算法、局部最大值算法以及提前过零检测算法;在步长推算阶段融合Weinberg方法和Kim方法,并利用卡尔曼滤波算法校正步数检测和步长推算的误差.基于不同场景从步数、步长、方向、位置误差方面与传统算法进行比较.结果表明,该组合模型结合了传统步数检测和步长推算算法的特征识别结果,可实现对步数检测、步长推算过程中信号特征的优化处理;在手持场景下,步数检测识别准确,步长推算中值误差在0.060 m以内,方向推算平均绝对误差最小为3.06°,位置推算平均误差为0.2353 m,取得较好的室内步行状态识别与定位性能.

To solve the problem that the pedestrian dead reckoning(PDR)indoor signals were susceptible to interference from environment and multipath effects,the optimal indoor PDR modelling method based on multi-model fusion was proposed.The system model of the multi-model fusion indoor PDR modelling approach was given with four key stages of step detection,step length projection,direction projection and position projection.In the step detection stage,the peak detection algorithm,local maximum algorithm and advance over zero detection algorithm were integrated,and in the step projection stage,the Weinberg method and Kim method were integrated.The Kalman filter algorithm was used to correct the errors of step detection and step projection.The comparison with traditional algorithms in terms of step number,step length,direction and position errors in different scenarios was completed.The results show that the fused model combines the feature recognition results of traditional step detection and step length estimation algorithms,which can realize the optimization of signal characteristics in the process of step detection and step length estimation.In the handheld scene,the step detection is accurate,and the step length estimation median error range is 0.060 m or less with the minimum direction estimation average absolute error of 3.06°and the position estimation average error of 0.2353 m,which achieves good indoor walking status recognition and position estimation performance.