基于无迹卡尔曼滤波的iBeacon/INS数据融合定位算法

王守华; 陆明炽; 孙希延; 纪元法; 胡丁梅

doi:10.11999/JEIT180748

基于无迹卡尔曼滤波的iBeacon/INS数据融合定位算法

doi: 10.11999/JEIT180748

王守华^{1, 2, ,},
陆明炽^{1, 2},
孙希延^{1, 2, 3},
纪元法^{1, 2, 3},
胡丁梅^{1, 2}

1.
桂林电子科技大学广西精密导航技术与应用重点实验室桂林 541004
2.
卫星导航定位与位置服务国家地方联合工程研究中心桂林 541004
3.
桂林电子科技大学广西信息科学实验中心桂林 541004

基金项目: 国家重点研发计划(2018YFB0505103)，广西自然科学基金（2018GXNSFAA050123），广西精密导航技术与应用重点实验室主任基金(DH201803)，广西科技项目(AA17202033)，桂林电子科技大学研究生教育创新计划项目(2018YJCX28)

详细信息

作者简介:
王守华：男，1975年生，副教授，研究方向为信号处理、卫星导航

陆明炽：男，1990年生，硕士生，研究方向为室内导航、深度学习

孙希延：女，1973年生，博士，研究方向为卫星导航和电子对抗

纪元法：男，1975年生，博士，研究方向为卫星通信、卫星导航和数字信号处理

胡丁梅：女，1995年生，硕士生，研究方向为室内导航、数据融合

通讯作者:
王守华　hwafly@guet.edu.cn

中图分类号: TN965.72
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出版历程
- 收稿日期: 2018-07-23
- 修回日期: 2019-02-25
- 网络出版日期: 2019-04-18
- 刊出日期: 2019-09-10

IBeacon/INS Data Fusion Location Algorithm Based on Unscented Kalman Filter

Shouhua WANG^{1, 2
, ,},
Mingchi LU^{1, 2},
Xiyan SUN^{1, 2, 3},
Yuanfa JI^{1, 2, 3},
Dingmei HU^{1, 2}

1.
Guangxi Key Laboratory of Precision Navigation Technology and Application, Guilin University of Electronic Technology, Guilin 541004, China
2.
Satellite Navigation and Location Service National & Local Joint Engineering Research Center, Guilin 541004, China
3.
Guangxi Experiment Center of Information Science, Guilin University of Electronic Technology, Guilin 541004, China

Funds: The National Key R&D Program of China (2018YFB0505103), The Foundation of Guangxi Natural Science Foundation (2018GXNSFAA050123), The Foundation of Guangxi Key Laboratory of Precision Navigation Technology and Application (DH201803), The Department of Science and Technology of Guangxi Zhuang Autonomous Region (AA17202033), The Innovation Project of Guet Graduate Education (2018YJCX28)

摘要

摘要: 针对微机电惯性导航系统(MEMS-INS)定位解算存在积累误差及低功耗蓝牙技术iBeacon指纹定位存在跳变误差等问题，该文提出一种基于无迹卡尔曼滤波器(UKF)的iBeacon/MEMS-INS数据融合定位算法。该算法对iBeacon锚点与定位目标的距离进行解算，利用加速度计和陀螺仪的数据实现姿态阵和位置解算。将蓝牙锚点位置向量、载体速度误差信息等组成状态量，将惯性导航定位信息和蓝牙定位距离信息等组成观测量，设计无迹卡尔曼滤波器，实现iBeacon/MEMS-INS数据融合定位。实验测试结果表明，该算法有效解决MEMS-INS存在较大积累误差及iBeacon指纹定位存在跳变误差的问题，可以实现1.5 m内的定位精度。
- 惯性传感器 /
- 蓝牙信标 /
- 无迹卡尔曼滤波器 /
- 信息融合 /
- 行人定位
Abstract: In order to overcome the accumulation error in Micro-Electro-Mechanical System-Inertial Navigation System (MEMS-INS) and the jump error in iBeacon fingerprint positioning, an iBencon/MEMS-INS data fusion location algorithm based on Unscented Kalman Filter (UKF) is proposed. The new algorithm solves the distance between the iBeacon anchor and the locating target. The solution of attitude matrix and position are obtained respectively by using accelerometer and gyroscope data. Bluetooth anchor position vector, the carrier speed error and other information constitute state variables. Inertial navigation location and bluetooth system distance information constitute measure variables. Based on state variables and measure variables, the UKF is designed to realize iBencon/MEMS-INS data fusion indoor positioning. The experimental results show that the proposed algorithm can effectively solve the problem of the large accumulation error of INS and the jump error of iBeacon fingerprint positioning, and this algorithm can realize 1.5 m positioning accuracy.
- Inertial sensor /
- Bluetooth beacon /
- Unscented Kalman Filter (UKF) /
- Information fusion /
- Pedestrian positioning

HTML全文

图 1 iBceacon/MEMS-INS组合导航算法方案

下载: 全尺寸图片幻灯片

图 2 UKF数据融合定位算法流程图

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图 3 实验平台基本架构

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图 4 iBeacon信标布置平面示意图

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图 5 加速度对比

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图 6 3种方法的定位轨迹

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图 7 定位误差CDF

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表 1 3DM模块各项指标

加速度计陀螺仪磁力计

轴数 3 3 3
量程 ±8 g ±1.700 °/s ±1/104 特斯拉(T)
数据更新频率 100 Hz 100 Hz 100 Hz

下载: 导出CSV

表 2 多种方法的性能比较

方法平均绝对位置误差(m) 运行时间(ms)

MEMS-INS 6.758 0.054
iBeacon 3.523 0.023
BLE/MEMS跨楼层 1.545 0.407
iBeacon/MEMS-INS 1.315 0.468

下载: 导出CSV

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