• 主题
高级搜索  >
历史搜索 清空历史
首页> 外文会议>Wireless Sensing, Local Positioning, and RFID, 2009. IMWS 2009 >An optimization approach for robust transformation of measured range data into position estimates in wireless networks
【24h】

An optimization approach for robust transformation of measured range data into position estimates in wireless networks

机译：一种用于将测量范围数据可靠地转换为无线网络中位置估计的优化方法

获取原文
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

This paper introduces a generalized framework for positioning mobile nodes in a wireless network. The transformation of the range data measured between pairs of network nodes into a spatial position is a very challenging task if the range measurements are distorted. Typical distortions in wireless ranging systems are caused by an imperfect clock synchronization or by multipath reflection. The positioning method proposed in this paper overcomes problems of the traditional circular or hyperbolic methods and allows for the detection and elimination of distorted measurements. The positioning is done in a non-Markovian manner, which is an advantage over other available methods, especially the Kalman-based positioning methods, since linear assumptions in the dynamics are avoided. Another advantage of the proposed method is that the two tasks positioning and smoothing are carried out separately. Hence, smoothing of the position data, e.g. by means of a Kalman filter, is possible without the well documented problems in conventional methods induced by the usually applied simplifying assumptions of linear dynamics. The very good performance of the presented approach is demonstrated by real life results obtained in industrial environments.
机译：本文介绍了用于在无线网络中定位移动节点的通用框架。如果距离测量值失真，将在成对的网络节点之间测量的距离数据转换为空间位置将是一项非常具有挑战性的任务。无线测距系统中的典型失真是由时钟同步不完善或多径反射引起的。本文提出的定位方法克服了传统的圆形或双曲线方法的问题，并允许检测和消除失真的测量结果。定位是以非马尔可夫方式完成的，这是相对于其他可用方法（尤其是基于卡尔曼的定位方法）的优势，因为避免了动力学中的线性假设。所提出的方法的另一个优点是，两个任务的定位和平滑分别进行。因此，对位置数据进行平滑处理，例如借助卡尔曼滤波器，可以避免常规方法中由通常采用的线性动力学简化假设引起的传统方法中没有充分记录的问题。在工业环境中获得的实际结果证明了所提出方法的出色性能。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱：kefu@zhangqiaokeyan.com

京公网安备：11010802029741号 ICP备案号：京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有

  • 客服微信

  • 服务号