一种基于蓝牙室内指纹定位的贝叶斯改进算法

doi:10.13474/j.cnki.11-2246.2019.0138

测绘通报 ›› 2019, Vol. 0 ›› Issue (5): 1-6.doi: 10.13474/j.cnki.11-2246.2019.0138

• 室内定位与导航 • 下一篇

一种基于蓝牙室内指纹定位的贝叶斯改进算法

郭英¹, 冯茗杨¹, 孙玉曦^1,2, 姬现磊¹, 刘清华¹

1. 山东科技大学测绘科学与工程学院, 山东青岛 266590;
2. 中国测绘科学研究院, 北京 100830

收稿日期:2018-10-17 出版日期:2019-05-25 发布日期:2019-06-04
作者简介:郭英(1973-),女,博士,副教授,主要研究方向为室内外高精度定位。E-mail:sdkdgy@163.com
基金资助:
国家重点研发计划（2016YFC0803102）；山东省重点研发计划（2018GGX106003）

An improved algorithm of Bayesian fingerprint localization based on bluetooth

GUO Ying¹, FENG Mingyang¹, SUN Yuxi^1,2, JI Xianlei¹, LIU Qinghua¹

1. College of Geomatics, Shandong University of Science and Technology, Qingdao 266590, China;
2. Chinese Academy of Surveying and Mapping, Beijing 100830, China

Received:2018-10-17 Online:2019-05-25 Published:2019-06-04

摘要/Abstract

摘要： 贝叶斯估计是重要的位置指纹定位算法，但传统的等值贝叶斯先验概率在动态定位中不适用。针对该问题，本文提出了一种基于贝叶斯指纹定位的改进算法。首先，借助陀螺仪获取的航向信息和高斯核函数模型建立概率投票算法，计算先验概率；然后，结合先验概率和信号强度计算待测点位于参考点上的后验概率；最后，选取概率最高的参考点，以概率为权重计算待测点的最或然值。以智能手机为试验对象，在规则路径试验中，改进算法的平均定位误差为1.15 m，定位误差小于2 m的概率为96.1%，不规则路径试验中，平均定位误差为0.50 m，定位误差在1 m的可信度为94.8%；并且改进算法对定位中位置跳变的现象有明显改善，具有较好的稳健性。

关键词: 室内定位, 低功耗蓝牙, 贝叶斯, 陀螺仪航向, 高斯核函数, 概率投票算法

Abstract: Bayesian estimation is an important position fingerprint localization algorithm, but the traditional equivalent Bayesian prior probability is not applicable in dynamic localization. In view of this problem, an improved algorithm based on Bayesian fingerprint localization is proposed in this paper. Firstly, by means of the heading information obtained by gyroscope and Gaussian kernel function model, the probabilistic voting algorithm is established to calculate the prior probability. Then, the prior probability combined with the signal strength is used to calculate the posterior probability of the point to be measured at the reference point. Finally, the most probable reference point is selected and the most probable value is calculated with the probability as the weight. In the regular path experiment, the average positioning error of the improved algorithm is 1.15 m, and the probability of positioning error less than 2 m is 96.1%. In the irregular path experiment, the average positioning error is 0.50 m, and the reliability of positioning error is 94.8%. In addition, the improved algorithm can improve the location hopping phenomenon and has good robustness.

Key words: indoor positioning, low energy bluetooth, Bayesian, gyroscopic heading, Gaussian kernel function, probabilistic voting algorithm

中图分类号:

P228

郭英, 冯茗杨, 孙玉曦, 姬现磊, 刘清华. 一种基于蓝牙室内指纹定位的贝叶斯改进算法[J]. 测绘通报, 2019, 0(5): 1-6.

GUO Ying, FENG Mingyang, SUN Yuxi, JI Xianlei, LIU Qinghua. An improved algorithm of Bayesian fingerprint localization based on bluetooth[J]. 测绘通报, 2019, 0(5): 1-6.

参考文献

[1] PETROVA K, WANG B. Location-based services deployment and demand:a roadmap model[J]. Electronic Commerce Research, 2011,11(1):5-29.
[2] AOMUMPAI S, KONDEE K, PROMMAK C, et al. Optimal placement of reference nodes for wireless indoor positioning systems[C]//Proceedings of the 11th International Conference on Electrical Engineering/Electronics, Computer, Telecommunications and Information Technology. Nakhon Ratchasima:IEEE, 2014:1-6.
[3] CHOW J C K, PETER M, SCAIONI M, et al. Indoor tracking, mapping, and navigation:algorithms, technologies, and applications[J]. Journal of Sensors, 2018(4):1-3.
[4] YIN Z, JIANG X, YANG Z, et al. WUB-IP:a high-precision UWB positioning scheme for indoor multiuser applications[J]. IEEE Systems Journal, 2018,PP(99):1-10.
[5] 陈锐志,陈亮.基于智能手机的室内定位技术的发展现状和挑战[J].测绘学报, 2017,46(10):1316-1326.
[6] 席瑞,李玉军,侯孟书.室内定位方法综述[J].计算机科学,2016,43(4):1-6,32.
[7] MA Z, POSLAD S, BIGHAM J, et al. A BLE RSSI ranking based indoor positioning system for generic smartphones[C]//Proceeding of 2017 Wireless Telecommunications Symposium. Chicago:IEEE, 2017:1-8.
[8] JANICKA J, RAPINSKI J. Application of RSSI based navigation in indoor positioning[C]//Proceedings of 2016 Baltic Geodetic Congress. Gdansk:IEEE, 2016.
[9] DAVIDSON P, PICHÉ R. A survey of selected indoor positioning methods for smartphones[J]. IEEE Communications Surveys and Tutorials, 2017,19(2):1347-1370.
[10] 曹鸿基,汪云甲,毕京学,等.一种顾及用户朝向的加权贝叶斯指纹定位方法[J].测绘科学, 2018,43(9):103-107,114.
[11] 游康勇,杨立山,刘玥良,等.基于稀疏贝叶斯学习的网格自适应多源定位[J].电子与信息学报,2018,40(9):2150-2157.
[12] O'HAGAN A, LEONARD T. Bayes estimation subject to uncertainty about parameter constraints[J]. Biometrika,1976,63(1):201-203.
[13] FEELDERS A, GAAG L C. Learning Bayesian network parameters under order constraints[J]. International Journal of Approximate Reasoning, 2006,42(1):37-53.
[14] 邵剑飞,叶阿勇.基于位置连续性的室内定位算法[J].福建师范大学学报(自然科学版), 2015,31(3):17-25.
[15] 王忠民, 陈振, 潘春华. 一种改进的位置指纹智能手机室内定位算法[J]. 西安邮电大学学报, 2014,19(1):17-20.
[16] FARAGHER R, HARLE R. Location fingerprinting with bluetooth low energy beacons[J]. IEEE Journal on Selected Areas in Communications, 2015, 33(11):2418-2428.
[17] 郭英,姬现磊,刘清华,等.基于手机陀螺仪航向修正算法[J].中国惯性技术学报, 2017,25(6):719-724.
[18] 王双成,高瑞,杜瑞杰.基于高斯Copula的约束贝叶斯网络分类器研究[J].计算机学报, 2016,39(8):1612-1625.
[19] ZAFARI F, PAPAPANAGIOTOU I, HACKER T J. A novel Bayesian filtering based algorithm for RSSI-based indoor localization[C]//Proceedings of 2018 IEEE International Conference on Communications. Kansas City:IEEE, 2018.
[20] CHEN Z, ZHU Q, SOH Y C. Smartphone inertial sensor-based indoor localization and tracking with iBeacon corrections[J]. IEEE Transactions on Industrial Informatics, 2016, 12(4):1540-1549.
[21] 龙英,何怡刚,张镇,等.基于信息熵和Haar小波变换的开关电流电路故障诊断新方法[J].仪器仪表学报, 2015,36(3):701-711.

一种基于蓝牙室内指纹定位的贝叶斯改进算法

An improved algorithm of Bayesian fingerprint localization based on bluetooth

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