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Multi-sensor Bayesian estimation interior positioning for stationary and mobile structures.

机译:固定和移动结构的多传感器贝叶斯估计内部定位。

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摘要

Indoor positioning has been a burgeoning technology which has been researched and, to date, sparsely implemented using a variety of sensors. This project addresses theoretical and practical considerations of a continuous outboard inboard positioning system with emphasis on its future implementation aboard Navy or commercial ships. The need for positioning systems aboard platforms, such as aircraft carriers, to reduce on-board manning, for damage control and maintenance, as well as for general personnel and high value asset tracking, is a Naval research initiative. The project evaluates, via analysis and demonstration, the utility of very low cost MEMS (gyroscopes and accelerometers) sensors, along with GPS, to augment the WiFi. This unique combination of sensors, which has been implemented in hardware and modelled in software, is used to explore the tradeoffs among positioning accuracy, availability and complexity.;The first test site for the experimental validation of the prototype system hardware and software was chosen as The George Washington University Ashburn campus in Northern Virginia. This site was chosen based on its extensive WiFi capability as well as it having certain areas with topological and WiFi transmission characteristics resembling that of a ship. A description of the WiFi network's access points, received signal strength maps, and overall topology is included. The generation of detailed apriori WiFi RSS maps along with the overall test methodology employed, necessary to demonstrate a horizontal performance accuracy goal of two meters and deck level vertical discrimination, are described. As a consequence of the non-linearity's involved in the RSS maps and topological constraints, as well as the non-Gaussian nature of some of the noise sources, emphasis has been placed on a novel implementation of a regularized particle filter.;This project considers navigation in both an absolute and relative sense. For most applications, absolute and relative solutions can be straightforwardly related to each other. A complication, however, arises when a user on a mobile platform employs inertial sensors as auxiliary sensors. These sensors inherently measure acceleration and angular rate with respect to inertial space and thus are subject to the total motion of the user. When the total motion of the user includes both a relative motion with respect to the mobile vehicle and a vehicle motion with respect to inertial space, then algorithms must attempt to separate the users' relative motion with respect to the mobile platform. This topic is analytically addressed in a preliminary fashion and will be the subject of further research. The project concludes with the plans and schedule for a second prototype demonstration aboard a US Navy ship in the Fall of 2009.
机译:室内定位已经成为一种新兴的技术,迄今为止,已经使用各种传感器对其进行了研究和稀疏实现。该项目解决了连续舷外舷内定位系统的理论和实践考虑,重点是海军或商用船在未来的实施。海军研究计划需要在诸如航空母舰之类的平台上定位系统,以减少机载人员,以进行损害控制和维护,以及用于一般人员和高价值资产追踪。该项目通过分析和演示评估了非常低成本的MEMS(陀螺仪和加速度计)传感器以及GPS的功能,以增强WiFi。这种传感器的独特组合已经在硬件中实现,并在软件中建模,用于探索定位精度,可用性和复杂性之间的折衷。选择第一个用于实验验证原型系统硬件和软件的测试站点作为位于北弗吉尼亚州的乔治华盛顿大学Ashburn校区。选择该站点的原因是它具有强大的WiFi功能,并且其某些区域具有类似于船舶的拓扑和WiFi传输特性。包括对WiFi网络访问点,接收信号强度图和整体拓扑的描述。描述了详细的先验WiFi RSS地图的生成以及所采用的整体测试方法,这对于证明两米的水平性能精度目标和甲板水平的垂直判别是必要的。由于RSS映射涉及非线性和拓扑约束,以及某些噪声源的非高斯性质,因此重点放在了正则化粒子滤波器的新颖实现上。绝对和相对意义上的导航。对于大多数应用,绝对解决方案和相对解决方案可以直接相互关联。然而,当移动平台上的用户采用惯性传感器作为辅助传感器时,会出现复杂情况。这些传感器固有地测量相对于惯性空间的加速度和角速度,因此要服从用户的总运动。当用户的总运动同时包括相对于移动车辆的相对运动和相对于惯性空间的车辆运动时,则算法必须尝试分离用户相对于移动平台的相对运动。本主题已通过初步分析解决,将成为进一步研究的主题。该项目以2009年秋季在美国海军舰船上进行第二次原型演示的计划和时间表为结尾。

著录项

  • 作者

    Tanju, Bereket R.;

  • 作者单位

    The George Washington University.;

  • 授予单位 The George Washington University.;
  • 学科 Engineering Naval.;Engineering Marine and Ocean.
  • 学位 Ph.D.
  • 年度 2010
  • 页码 222 p.
  • 总页数 222
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

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