[1]李旭虹,张同丽,张育芝.矿井NLOS环境下改进UKF超宽带定位算法[J].西安科技大学学报,2018,(05):824-829.[doi:10.13800/j.cnki.xakjdxxb.2018.0518]
 LI Xu-hong,ZHANG Tong-li,ZHANG Yu-zhi.Research on improved UKF UWB localization algorithm in mine NLOS environment[J].Journal of Xi'an University of Science and Technology,2018,(05):824-829.[doi:10.13800/j.cnki.xakjdxxb.2018.0518]
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矿井NLOS环境下改进UKF超宽带定位算法(/HTML)
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西安科技大学学报[ISSN:1672-9315/CN:61-1434/N]

卷:
期数:
2018年05期
页码:
824-829
栏目:
出版日期:
2018-09-30

文章信息/Info

Title:
Research on improved UKF UWB localization algorithm in mine NLOS environment
文章编号:
1672-9315(2018)05-0830-07
作者:
李旭虹张同丽张育芝
西安科技大学 通信与信息工程学院,陕西 西安 710054
Author(s):
LI Xu-hongZHANG Tong-liZHANG Yu-zhi
College of Communication and Information Engineering,Xi'an University of Science and Technology,Xi'an 710054,China
关键词:
通信工程 超宽带 非视距 接收信号强度 无迹卡尔曼滤波 定位
Keywords:
communication engineering UWB NLOS RSSI UKF location
分类号:
TN 929.53
DOI:
10.13800/j.cnki.xakjdxxb.2018.0518
文献标志码:
A
摘要:
由于矿井下巷道空间有限且存在弯曲,因此通信传播大多为非视距(NLOS)传播环境。传统的基于距离的无线传感网络定位技术易产生较大误差,从而导致定位精度不高。超宽带(UWB)信号具有穿透能力强、抗多径能力强的特点,能够提供较高的定位精度,因此成为NLOS环境下定位的首选。针对NLOS环境下的定位精度不高的问题,在接收信号强度(RSSI)测距和三边算法联合定位的基础上,提出以联合定位得到的初始坐标做为无迹卡尔曼滤波(UKF)算法的观测值,并对UKF的测量更新方程进行修正,使该算法能适应NLOS环境下的定位跟踪。通过仿真验证并与扩展卡尔曼滤波(EKF)算法进行比较,在NLOS环境下改进的UKF定位算法,能够对目标进行实时跟踪并提高定位的精度。
Abstract:
Due to the limited space and bend in the tunnel under the mine,communication propagation is largely non-line of sight(NLOS)propagation environment.The traditional localization technology of wireless sensor network based on distance causing large errors easily,thus the positioning accuracy is low.Ultra wideband(UWB)signals have the characteristics of strong penetrating ability and strong anti multipath capability,providing a high positioning accuracy.Therefore it becomes the preferred location method in NLOS environment aimed at the location and tracking problem in NLOS environment,initial positioning coordinate was achieved by Received Signal Strength Indication(RSSI)and three side algorithm,and the initial coordinates obtained by joint positioning as the observation value of Untraced Kalman filter(UKF)algorithm.The measurement update equation of UKF was modified so that it could adapt to the tracking and positioning in NLOS environment.Compared with the extended Kalman filter(EKF)algorithm,the improved UKF localization algorithm in NLOS environment can track the target in real time and improve the positioning accuracy.

参考文献/References:

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备注/Memo

备注/Memo:
收稿日期:2018-01-21 责任编辑:高 佳
基金项目:陕西省自然科学基础研究计划资助项目(2015JM6341)
通信作者:李旭虹(1970-),女,新疆乌鲁木齐人,副教授,E-mail:lixhong105@xust.edu.cn
更新日期/Last Update: 2018-09-30