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Real-time Cycle-slip Detection and Correction for Land Vehicle Navigation using Inertial Aiding.

机译:使用惯性辅助的陆地车辆导航的实时周跳检测和校正。

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摘要

Processing GPS carrier-phase measurements can provide high positioning accuracy for several navigation applications. However, if not detected, cycle slips in the measured phase can strongly deteriorate the positioning accuracy. Cycle slips frequently occur in areas surrounded by trees, buildings, and other obstacles. The dynamics experienced by the GPS receiver in kinematic mode of navigation also increases the possibility of cycle slips. Detection and correction of these cycle-slips is essential for reliable navigation. One way of detecting and correcting for cycle slips is to use another system to be integrated with GPS. Inertial Navigation Systems (INS), using three-axis accelerometers and three-axis gyroscopes, is integrated with GPS to provide more reliable navigation solution. Moreover, INS was utilized in the past for GPS cycle slip detection and correction. For low cost applications, Micro- Electro-Mechanical-Systems (MEMS) accelerometers and gyroscopes are used inside INS. For land navigation, reduced inertial sensor system (RISS) utilizing two accelerometers, one gyroscope, and the vehicle odometer was suggested. MEMS-based RISS has the advantage of using less number of MEMS-based gyroscopes and accelerometers thus reducing the overall cost and avoiding the complex error characteristics associated with MEMS sensors. In this thesis, we investigate the use of MEMS -- based RISS to aid GPS and detect and correct for cycle slips. The Kalman filter was employed in centralized fashion to integrate the measurements from both GPS and RISS. This thesis research also offers a new threshold selection criterion resulting in a more robust cycle slip detection and correction. The proposed method was tested in different scenarios of road tests in land vehicle. Results show accuracy improvement over the conventional double differenced pseudoranges-based integrated system. Moreover, the adaptive selection criterion of the detection threshold proposed in this thesis improves the detection rate, especially in the case of small-sized cycle slips.
机译:处理GPS载波相位测量可以为多种导航应用提供高定位精度。但是,如果未检测到,则测量相位的周跳会严重降低定位精度。自行车打滑经常发生在被树木,建筑物和其他障碍物包围的区域。 GPS接收器在运动学的导航模式中所经历的动力学也增加了周跳的可能性。对这些周跳的检测和校正对于可靠的导航至关重要。检测和校正周跳的一种方法是使用另一种与GPS集成的系统。使用三轴加速度计和三轴陀螺仪的惯性导航系统(INS)与GPS集成在一起,可提供更可靠的导航解决方案。此外,INS过去曾用于GPS周期滑移检测和校正。对于低成本应用,INS内部使用了微机电系统(MEMS)加速度计和陀螺仪。对于陆地导航,建议使用两个加速度计,一个陀螺仪和车辆里程表的简化惯性传感器系统(RISS)。基于MEMS的RISS具有使用较少数量的基于MEMS的陀螺仪和加速度计的优势,从而降低了总体成本并避免了与MEMS传感器相关的复杂误差特性。在本文中,我们研究了基于MEMS的RISS的用途,以辅助GPS并检测和校正周跳。卡尔曼滤波器以集中方式使用,以集成来自GPS和RISS的测量结果。本文的研究还提供了一种新的阈值选择准则,从而使循环滑差检测和校正更加可靠。所提出的方法在陆地车辆的道路测试的不同场景中进行了测试。结果表明,与传统的基于双差伪距的集成系统相比,精度有所提高。此外,本文提出的检测阈值的自适应选择准则提高了检测率,特别是在小周期滑动的情况下。

著录项

  • 作者

    Karaim, Malek Omar.;

  • 作者单位

    Queen's University (Canada).;

  • 授予单位 Queen's University (Canada).;
  • 学科 Computer Science.
  • 学位 M.A.Sc.
  • 年度 2013
  • 页码 122 p.
  • 总页数 122
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

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