[1]郭 琛,邵建华,柯 炜,等.基于指纹的可见光室内定位系统融合算法[J].南京师范大学学报(工程技术版),2019,(01):058.[doi:10.3969/j.issn.1672-1292.2019.01.008]
 Guo Chen,Shao Jianhua,Ke Wei,et al.A Visible Light Indoor Positioning Algorithm Based on Fingerprint[J].Journal of Nanjing Normal University(Engineering and Technology),2019,(01):058.[doi:10.3969/j.issn.1672-1292.2019.01.008]
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基于指纹的可见光室内定位系统融合算法
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南京师范大学学报(工程技术版)[ISSN:1006-6977/CN:61-1281/TN]

卷:
期数:
2019年01期
页码:
058
栏目:
信息工程
出版日期:
2019-03-30

文章信息/Info

Title:
A Visible Light Indoor Positioning Algorithm Based on Fingerprint
文章编号:
1672-1292(2019)01-0058-07
作者:
郭 琛1邵建华12柯 炜12张春艳1安 爽1
(1.南京师范大学物理科学与技术学院,江苏 南京 210023)(2.南京师范大学江苏省光电技术重点实验室,江苏 南京 210023)
Author(s):
Guo Chen1Shao Jianhua1Ke Wei12Zhang Chunyan1An Shuang1
(1.School of Physics and Technology,Nanjing Normal University,Nanjing 210023,China)(2.Key Laboratory for Opto-Electronic Technology of Jiangsu Province,Nanjing Normal University,Nanjing 210023,China)
关键词:
室内定位指纹KNN贝叶斯
Keywords:
indoor positioningfingerprintKNNBayes
分类号:
TN929.1
DOI:
10.3969/j.issn.1672-1292.2019.01.008
文献标志码:
A
摘要:
可见光室内定位系统由于墙壁反射和外界噪声存在而产生误差. 对现有的基于指纹识别的可见光室内定位算法进行仿真比较,提出了将确定型算法与概率分布算法融合的室内定位改进算法. 首先用KNN算法选取几个与接收机位置相近的网格点,对接收信号进行高斯滤波,而后用贝叶斯算法计算其后验概率,后验概率最大的点即为估计位置. 这一改进算法不仅降低了贝叶斯算法的复杂度,也大大提高了KNN算法的定位精度,平均误差为0.17 m.
Abstract:
Errors are caused by wall reflection and noise in the visible light indoor positioning system. Some indoor positioning algorithms based on fingerprint are compared through simulation. An improved indoor positioning algorithm that combines K-Nearest Neighbor and Bayesian theory algorithm is proposed. Firstly,several grid points which are close to the receiver’s position are selected by KNN algorithm,after the RSSI sampling value through Gauss filter is processed,then the posterior probability is calculated by Bayesian algorithm. The point with the largest posterior probability is the estimated position. The improved algorithm simplifies the Bayesian algorithm and improves the positioning accuracy of KNN algorithm,with an average error of 0.17 m.

参考文献/References:

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备注/Memo

备注/Memo:
收稿日期:2018-04-17.
通讯联系人:邵建华,教授,研究方向:通信技术. E-mail:shaojianhua@njnu.edu.cn
更新日期/Last Update: 2019-03-30