基于深度置信网络的室内指纹定位算法

doi:10.3969/j.issn.1000-1565.2021.03.015

河北大学学报(自然科学版) ›› 2021, Vol. 41 ›› Issue (3): 321-328.DOI: 10.3969/j.issn.1000-1565.2021.03.015

基于深度置信网络的室内指纹定位算法

秦益文¹,封志宏¹,苏楠²,马丁¹,李小菲³

收稿日期:2020-02-24 发布日期:2021-05-28
通讯作者: 李小菲(1979—)
作者简介:秦益文(1996—),女,河北石家庄人,兰州交通大学在读硕士研究生,主要从事深度学习、指纹定位研究.
E-mail:qinyiwen2009@126.com
基金资助:
河北省自然科学基金资助项目(F2019201427);教育部“云数融合科教创新”基金资助项目(2017A20004)

Indoor finerprint localization algorithm based on deep belif network

QIN Yiwen¹, FENG Zhihong¹, SU Nan², MA Ding¹, LI Xiaofei³

1.School of Electric and Information Engineering, Lanzhou Jiaotong University, Lanzhou 730070, China; 2.School of Cyber Security and Computer, Hebei University, Baoding 071002, China; 3.Information Technology Center, Hebei University, Baoding 071002, China

Received:2020-02-24 Published:2021-05-28

摘要/Abstract

摘要： 基于WLAN的指纹识别技术是在每个指纹定位测量来自不同APs的接收信号强度RSS(received signal strength,RSS)以构建指纹.然而,指纹的收集由于环境的变化,需要定期更新指纹库以提高准确性.因此,为减少指纹识别的工作量,提出将深度置信网络算法应用到未标记的RSS测量中,利用生成概率模型来表示不同隐含层的层次隐含特征,提取指纹的隐藏特征,通过预训练和调优阶段,从而在尽可能保持定位精度的前提下减少标记指纹.实验结果表明,本算法在仅使用15%标记指纹时,将定位精度提高了1.876 m.

关键词: 指纹定位, 深度置信网络, RSS, 特征提取, 无监督学习

Abstract: WLAN-based fingerprint recognition technology measures Received Signal Strength(RSS)from different APs at a given location of each fingerprint to construct the fingerprint. However, due to changes in the environment, fingerprint collection needs to be updated regularly to improve accuracy. Therefore, in order to reduce the workload of fingerprint identification, the deep belief network algorithm is applied to the unlabelled RSS measurements, to generate probability model to represent the level of the different hidden layer implied characteristics, to extract the hidden features of fingerprint through preliminary training and tuning phase, and to keep the positioning accuracy under the premise of reducing tag fingerprints as much as possible. Experimental results show that this algorithm improves the positioning accuracy by 1.876m when only 15% of the fingerprint is used.

Key words: fingerprint localization, deep belief network, RSS, feature extraction, unsupervised learning

中图分类号:

TP391

秦益文,封志宏,苏楠,马丁,李小菲. 基于深度置信网络的室内指纹定位算法[J]. 河北大学学报(自然科学版), 2021, 41(3): 321-328.

QIN Yiwen, FENG Zhihong, SU Nan, MA Ding, LI Xiaofei. Indoor finerprint localization algorithm based on deep belif network[J]. Journal of Hebei University(Natural Science Edition), 2021, 41(3): 321-328.

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基于深度置信网络的室内指纹定位算法

Indoor finerprint localization algorithm based on deep belif network

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