基于卡尔曼滤波的车辆运行数据估计

Haifeng Song, Minjie Zhang, Kai Feng, Jianfeng Cheng, Datian Zhou
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引用次数: 0

摘要

起伏的地形可能导致路线坡度的改变。当车辆在该路线的不同路段行驶时,车辆的姿态可能会有所波动。利用俯仰姿态数据确定车辆位置是一种新颖的原理。本文提出了一种基于DTW (Dynamic Time Warping)的方法,对惯性测量单元(IMU)数据积累的定位算法进行了改进。该方法用于识别时序俯仰角序列与存储路线波动特征的数字地图之间的匹配。通过对周期累积距离误差的估计,验证了该方法的有效性。
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Kalman filter Based Vehicle Running Data Estimation
The terrain of undulation might lead to change the slope of a route. During a vehicle moving in different section of such route, the attitude of the vehicle might fluctuate respectively. It is a novel principle of using the attitude data of pitch to determine a vehicle’s position. This paper presents a method based on DTW (Dynamic Time Warping), which augments the location algorithm based on accumulating data from IMU (Inertial Measurement Unit). This method is designed to recognize a match between pitch angle sequence by time and a digital map storing undulatory characters of a route. The effectiveness of the presented method is validated by estimating errors of distance accumulated in periods.
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