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Multisensor data fusion applied to marine integrated navigation systems

机译:多传感器数据融合应用于船舶集成导航系统

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摘要

This paper deals with the problem of designing a flexible and accurate integrated navigation system (INS) for marine craft. The proposed INS is based on the integration of a global positioning system (GPS) with a compass and a speed log. After introducing the scopes and functions of the proposed INS, mathematical models of its main components are presented. Then the development of a new multisensor data fusion algorithm for carrying out an accurate estimation of the main state variables is presented. The theoretical background for the sensor fusion is based on the classical Kalman filter theory, which allows to update an a priori position estimate, given by a dead-reckoning system, with the information supplied by a GPS positioning system. Finally the filtering algorithm is extended in the framework of interval analysis and fuzzy set theory in order to improve the reliability and robustness of the estimation algorithm. The validity of the proposed approach is demonstrated by simulation examples applied to a container ship navigating in realistic conditions.
机译:本文涉及为船舶设计一种灵活,准确的集成导航系统(INS)的问题。拟议的惯导系统是基于将全球定位系统(GPS)与指南针和速度记录仪集成在一起的。在介绍了拟议的惯性导航系统的范围和功能之后,提出了其主要组成部分的数学模型。然后提出了一种新的多传感器数据融合算法的开发方法,该算法可以对主状态变量进行精确估计。传感器融合的理论背景基于经典的卡尔曼滤波理论,该理论允许使用GPS定位系统提供的信息来更新死角复航系统给出的先验位置估计。最后,在区间分析和模糊集理论的框架内扩展了滤波算法,以提高估计算法的可靠性和鲁棒性。通过在实际条件下航行的集装箱船上的仿真实例证明了该方法的有效性。

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