电子学报 ›› 2009, Vol. 37 ›› Issue (2): 382-386.

• 论文 • 上一篇    下一篇

普适计算中的定位误差分析

周 艳1,2, 赵 海1, 张 君1, 李海成2   

  1. 1. 东北大学信息科学与工程学院,辽宁沈阳 110004;2. 辽东学院信息技术分院,辽宁丹东 118003
  • 收稿日期:2007-08-08 修回日期:2008-10-27 出版日期:2009-02-25 发布日期:2009-02-25

Location Error Analysis of Pervasive Computing

ZHOU Yan1,2, ZHAO Hai1, Zhang Jun1, LI Hai-cheng2   

  1. 1. School of Information Science and Engineering,Northeastern University,Shenyang,Liaoning 110004,China;2. Information Technology College,Liaodong University,Dandong,Liaoning 118003,China
  • Received:2007-08-08 Revised:2008-10-27 Online:2009-02-25 Published:2009-02-25

摘要: 在二维空间定位服务中,通过对定位过程中产生的误差区域进行分析,提出了参考点优化选择定理,参考点优化选择定理表明在室内定位过程中有针对性选择参考点能使定位误差最小,为室内环境中布置和选择定位参考点提供了相应的理论基础.在此基础上,对传统定位算法进行了改进,提出了定位参考点优化选择算法(RNOS).RNOS算法以参考点与未知节点之间位置关系为基础,通过选择出合适的参考点来计算未知节点的位置,可以提供更准确的定位信息.仿真实验表明本文所提出的参考点优化选择算法能更好地满足对普适终端实时定位的需求,且具有较高的定位精度.

关键词: 普适计算, 定位误差, 定位参考点, 优化选择, 参考点优化选择算法

Abstract: In the localization service of two dimensional space,reference nodes optimizing selection theorem in two dimensional space is proposed on the basis of analyzing the location error areas.The theorem of refeence nodes optimizing selection indicates that purposive selection of reference nodes will minimize the location error in the process of indoor localization.Meanwhile this theorem builds up theoretical foundation for the layout and selection of reference nodes in indoor environment.Based on this, reference nodes optimization selection algorithm(RNOS Algorithm)is proposed by improving the traditional polygon positioning algorithm.This algorithm is based on the relationship between the position of reference nodes and the position of unknown node.By selecting optimum reference nodes in the process of calculating the unknown nodes’ position,the more accurate location information can be obtained.The simulation results indicate that the location reference node optimization selection(RNOS)algorithm can meet the requirement of pervasive terminal’s real-time localization and possesses the preferable localization precision.

Key words: pervasive computing, location error, location refrence node, optimizing selection, reference node optimization selection algorithm

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