IMM EKF based Sensor Fusion for Vehicle Positioning Under Various Road Surface Conditions

Hyeong Heo, Dae Jung Kim, C. Chung
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引用次数: 2

Abstract

In this paper, we propose an estimation of the accurate vehicle position using Interacting Multiple Model Extended Kalman Filter (IMM EKF) when road surface varies. Since the vehicle has different cornering stiffness as the road surface varies, it is difficult to accurately estimate the position of the vehicle. To resolve this problem, we present the IMM EKF considering each model of different roads to improve the estimation performance. From the numerical simulation using MATLAB/CARSIM, we observed that the performance of the proposed algorithm improves vehicle positioning performance.
基于IMM EKF的传感器融合在不同路面条件下的车辆定位
本文提出了一种基于交互多模型扩展卡尔曼滤波(IMM EKF)的路面变化情况下车辆精确位置估计方法。由于车辆的转弯刚度随路面的变化而变化,难以准确估计车辆的位置。为了解决这一问题,我们提出了考虑不同道路模型的IMM EKF,以提高估计性能。通过MATLAB/CARSIM的数值模拟,我们观察到该算法的性能提高了车辆定位性能。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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