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A Novel Approach for Modeling Land Vehicle Kinematics to Improve GPS Performance Under Urban Environment Conditions

机译:一种在城市环境条件下提高GPS性能的陆地车辆运动学建模的新方法

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摘要

The satellite blockage problem, which adversely affects Global Positioning System (GPS) accuracy in urban environments, is addressed in this work. To provide a position solution, there must be at least four satellites within line of sight (LOS) of the receiver (vehicle). However, when satellite blockage occurs, this requirement is not met because most of the sky is obscured by tall buildings, and only a narrow sky sector is exposed to the receiver. Given the short duration of satellite blockages, an efficient solution to this problem can be accomplished through reliable modeling of vehicle motion. In this manner, information regarding vehicle motion can be more precisely obtained in the absence of a sufficient number of measurements. In this paper, a model that uses intrinsic quantities as part of the description of the vehicle path, such as curvature, tangent angle, and tangential speed, is proposed to achieve a more accurate modeling of both the trajectory and kinematics of a land vehicle. The model, which was implemented via an extended Kalman filter, was examined against currently known models during satellite blockage scenarios and demonstrated superior performance.
机译:这项工作解决了卫星阻塞问题,该问题会对城市环境中的全球定位系统(GPS)准确性产生不利影响。为了提供位置解决方案,接收器(车辆)的视线(LOS)内必须至少有四个卫星。但是,当发生卫星阻塞时,由于大部分天空都被高大的建筑物遮盖了,并且只有狭窄的天空区域暴露在接收器中,因此无法满足此要求。考虑到卫星阻塞的持续时间很短,可以通过对车辆运动进行可靠的建模来实现对该问题的有效解决方案。以此方式,在没有足够数量的测量的情况下,可以更精确地获得关于车辆运动的信息。在本文中,提出了一个模型,该模型使用内在量作为车辆路径描述的一部分,例如曲率,切线角和切线速度,以实现对陆地车辆的轨迹和运动学的更精确建模。该模型是通过扩展的卡尔曼滤波器实现的,在卫星阻塞情况下针对当前已知的模型进行了检查,并展示了出色的性能。

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