基于时间序列匹配的过山车定位法

Roller coaster localization based on time series alignment

针对过山车难以精准定位的问题,本文提出了一种基于时间序列匹配的过山车定位方法。该方法首先使用动态时间规整(DTW)对惯性测量单元(IMU)的实测与仿真数据进行序列匹配,得到位置估计结果。之后将估计结果作为观测量,在误差状态卡尔曼滤波器(ESKF)中修正IMU预测结果,得到精准的定位结果。为了提高估计结果的准确度,本文提出了分段重组动态时间规整(SRDTW)算法,解决了DTW的匹配失真问题。使用本文方法对过山车进行了定位实验,结果表明,使用Z向加速度和俯仰角进行序列匹配可得到较为准确的估计结果;ESKF滤波后的平均定位误差可达0.24 m,较估计结果的定位误差减小45.6%。

To address the problem that roller coasters are difficult to locate,this article proposes a roller coaster localization method based on time series alignment.The method firstly uses the dynamic time warping(DTW)to align the inertial measurement unit(IMU)measured data with the simulated data to obtain the estimated position of roller coaster.Subsequently,the error state Kalman filter(ESKF)is used to update the IMU-based prediction by using the DTW estimation as observation.To improve the accuracy of the estimation,the segment recombination dynamic time warping(SRDTW)algorithm is proposed to solve the distortion problem of DTW.The localization experiments of a roller coaster are conducted using the method in this article.The results show that time series alignment using Z-directional acceleration and pitch angle can provide more accurate position estimation,and the average error after ESKF filtering can reach 0.24 m,which is 45.6%lower than average error of estimation alone.