电子学报 ›› 2016, Vol. 44 ›› Issue (5): 1174-1179.DOI: 10.3969/j.issn.0372-2112.2016.05.023

• 学术论文 • 上一篇    下一篇

面向非视距环境的室内定位算法

毛科技1, 邬锦彬1, 金洪波1, 苗春雨1,2, 夏明1, 陈庆章1   

  1. 1. 浙江工业大学计算机科学与技术学院, 浙江杭州 310023;
    2. 浙江师范大学行知学院, 浙江金华 321004
  • 收稿日期:2015-06-01 修回日期:2015-12-07 出版日期:2016-05-25 发布日期:2016-05-25
  • 通讯作者: 毛科技
  • 作者简介:邬锦彬 男,1991年生于浙江省宁波市.浙江工业大学硕士研究生在读,主要研究方向为无线传感器网络节点定位技术.E-mail:wujinbin91@foxmail.com
  • 基金资助:

    国家自然科学基金(No.61379023,No.61401397);浙江省自然科学基金(No.LY14F020020);浙江省公益性技术应用研究计划项目(No.2015C31066);浙江省计算机科学与技术重中之重学科(浙江师范大学)资助课题(No.ZC323014074)

Indoor Localization Algorithm for NLOS Environment

MAO Ke-ji1, WU Jin-bin1, JIN Hong-bo1, MIAO Chun-yu1,2, XIA Ming1, CHEN Qing-zhang1   

  1. 1. College of Computer Science and Technology, Zhejiang University of Technology, Hangzhou, Zhejiang 310023, China;
    2. College of Xingzhi, Zhejiang Normal University, Jinhua, Zhejiang 321004, China
  • Received:2015-06-01 Revised:2015-12-07 Online:2016-05-25 Published:2016-05-25

摘要:

节点位置信息在无线传感器网络中起着至关重要的作用.大多数定位算法在视距(Line-of-Sight,LOS)环境下能够取得较高的定位精度,然而在非视距(Non-Line-of-Sight,NLOS)环境下,由于障碍物的阻挡,无法取得理想的定位精度.针对室内环境中普遍存在的非视距传播现象,提出了基于RTT(Round Trip Time)和AOA(Angle Of Arrival)混合测距方式的室内定位方法,一种轻量级基于网格的聚类算法(Lightweight Grid-Based Cluster,LGBC)被用来生成移动节点的定位区域.算法不需要获取室内环境的先验信息.仿真结果表明,LGBC算法复杂度低,计算开销小,并且与同类算法相比,定位精度提高约65%.

关键词: 无线传感器网络, 室内定位, 非视距环境, 聚类

Abstract:

Location of sensor plays a pivot role in WSNs.Most of the localization algorithms can achieve extremely high positioning accuracy in line of sight (LOS) environment.However, they are unable to obtain ideal accuracy due to the obstacles in non-line of sight (NLOS) environment.In order to solve the NLOS propagation problem in indoor environment, we propose an indoor localization method based on RTT and AOA using a lightweight grid-based clustering (LGBC) algorithm.The LGBC algorithm does not depend on any prior information of indoor environment and possesses significant flexibility.The simulation results show that LGBC algorithm has low time complexity and small computational overhead.Furthermore, it outperforms the other method by about 65 percent in terms of localization accuracy.

Key words: WSNs(wireless sensor networks), indoor localization, NLOS(non line of sight) environment, clustering

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