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Intelligent MEMS INS/GPS integration for land vehicle navigation .

机译：用于陆地车辆导航的智能MEMS INS / GPS集成。

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摘要

Although Global Positioning System (GPS) has been widely used to land vehicle navigation systems, GPS is unable to provide continuous and reliable navigation solutions in the presence of signal fading and/or blockage such as in urban areas. With the advent of the Micro-Electro-Mechanical System (MEMS) Inertial Navigation System (INS), a low-cost MEMS INS/GPS integration system becomes available to provide improved navigation performance by integrating the long-term GPS accuracy with the short-term INS accuracy. The challenges to low-cost MEMS INS/GPS integration arise from dealing with the corrupted GPS data in signal-degraded environments, the large instrument errors experienced with low-grade MEMS sensors and the distorted magnetic measurements from an embedded electronic compass. This dissertation develops intelligent data fusion and processing techniques for such a low-cost integration system by incorporating the Artificial Intelligence (AI) with the Kalman filtering.; Two cascaded Kalman filters implemented upon a loosely coupled integration scheme are applied to perform data fusion in the velocity/attitude and position domain, respectively. Three AI-based methods are developed for GPS data assessment, INS error control and compass error modelling to enhance the Kalman-filter-based integration. Specifically, a fuzzy GPS data classification system is developed to optimize INS/GPS data fusion through adjusting the measurement covariances of the Kalman filters according to GPS signal degradation conditions. A dynamics knowledge aided inertial navigation algorithm along with a fuzzy expert vehicle dynamics identification system is created to reduce and control INS error drift through simplifying system models and extending measurement update schemes of the Kalman filters. A neural-networks-based compass calibration algorithm is developed to provide the correct compass heading updates to the Kalman filters in the presence of disturbance.; The developed algorithms have been tested and evaluated in various GPS conditions, which include open areas, complete GPS outages and urban areas, using a low-cost Xsens MT9 MEMS IMU and SiRF Star II conventional/high sensitivity GPS receivers. The obtained results have confirmed the effectiveness of the AI-based methods and the significant performance improvement by the intelligent integration algorithm. For GPS outages around 3 minutes, the intelligent integration system is able to maintain satisfactory position accuracy with the maximum error less than 30 m. In the typical North American urban canyons, the intelligent integration system can provide continuous and reliable navigation solutions with the horizontal position accuracy of around 15 m. Overall results confirm the benefits and advantages of applying the developed AI methods to assist the low-cost MEMS INS/GPS integration for land vehicle navigation.
机译：尽管全球定位系统（GPS）已被广泛用于陆地车辆导航系统，但是GPS在存在信号衰落和/或阻塞的情况下（例如在城市地区）无法提供连续且可靠的导航解决方案。随着微机电系统（INS）惯性导航系统（INS）的到来，低成本的MEMS INS / GPS集成系统变得可以通过将长期GPS精度与短时GPS集成在一起来提供改进的导航性能。术语INS精度。低成本MEMS INS / GPS集成面临的挑战来自在信号退化的环境中处理损坏的GPS数据，低级MEMS传感器遇到的大型仪器错误以及嵌入式电子罗盘产生的磁测量失真。本文通过将人工智能（AI）与卡尔曼滤波相结合，为这种低成本的集成系统开发了智能数据融合和处理技术。应用在松耦合积分方案上实现的两个级联卡尔曼滤波器分别在速度/高度和位置域中执行数据融合。开发了三种基于AI的方法来进行GPS数据评估，INS误差控制和罗盘误差建模，以增强基于Kalman滤波器的集成。具体而言，开发了模糊GPS数据分类系统，通过根据GPS信号劣化条件调整卡尔曼滤波器的测量协方差来优化INS / GPS数据融合。创建了动力学知识辅助惯性导航算法以及模糊专家车辆动力学识别系统，以通过简化系统模型和扩展Kalman滤波器的测量更新方案来减少和控制INS误差漂移。开发了一种基于神经网络的指南针校准算法，以在存在干扰的情况下为Kalman滤波器提供正确的指南针航向更新。使用低成本的Xsens MT9 MEMS IMU和SiRF Star II常规/高灵敏度GPS接收器，已在各种GPS条件下对开发的算法进行了测试和评估，包括开放区域，完整的GPS中断和市区。获得的结果证实了基于AI的方法的有效性以及智能集成算法的显着性能改进。对于3分钟左右的GPS中断，智能集成系统能够保持令人满意的位置精度，最大误差小于30 m。在典型的北美城市峡谷中，智能集成系统可以提供连续可靠的导航解决方案，其水平位置精度约为15 m。总体结果证实了使用开发的AI方法协助低成本MEMS INS / GPS集成用于陆地车辆导航的好处和优点。

著录项

  • 作者

    Wang, Jau-Hsiung.;

  • 作者单位

    University of Calgary (Canada).;

  • 授予单位 University of Calgary (Canada).;
  • 学科 Engineering Automotive.
  • 学位 Ph.D.
  • 年度 2006
  • 页码 238 p.
  • 总页数 238
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类 自动化技术及设备;
  • 关键词

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