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山东大学学报 (工学版) ›› 2018, Vol. 48 ›› Issue (5): 95-102.doi: 10.6040/j.issn.1672-3961.0.2018.169

• 控制科学与工程 • 上一篇    下一篇

基于集成深度神经网络的室内无线定位

沈冬冬(),周风余*(),栗梦媛,王淑倩,郭仁和   

  1. 山东大学控制科学与工程学院, 山东 济南 250061
  • 收稿日期:2018-05-02 出版日期:2018-10-01 发布日期:2018-05-02
  • 通讯作者: 周风余 E-mail:shendongdong724@foxmail.com;zhoufengyu@sdu.edu.cn
  • 作者简介:沈冬冬(1992—)，男，安徽安庆人，硕士研究生，主要研究方向为深度学习. E-mail:shendongdong724@foxmail.com
  • 基金资助:
    国家重点研发计划(2017YFB1302400);国家自然科学基金资助项目(61773242);山东省重大科技创新工程资助项目(2017CXGC0926);山东省重点研发计划资助项目(公益类专项)(2017GGX30133)

Indoor wireless positioning based on ensemble deep neural network

Dongdong SHEN(),Fengyu ZHOU*(),Mengyuan LI,Shuqian WANG,Renhe GUO   

  1. School of Control Science and Engineering, Shandong University, Jinan 250061, Shandong, China
  • Received:2018-05-02 Online:2018-10-01 Published:2018-05-02
  • Contact: Fengyu ZHOU E-mail:shendongdong724@foxmail.com;zhoufengyu@sdu.edu.cn
  • Supported by:
    国家重点研发计划(2017YFB1302400);国家自然科学基金资助项目(61773242);山东省重大科技创新工程资助项目(2017CXGC0926);山东省重点研发计划资助项目(公益类专项)(2017GGX30133)

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摘要：

针对传统无线定位模型对指纹数据库容错性低、抗噪能力弱等问题,提出一种基于数据融合的集成深度神经网络无线定位方法,从原始指纹数据库中按照一定比例随机取样生成各基学习器的训练数据,能够有效克服异常样本与有噪数据对无线定位系统带来的干扰;在指纹数据库构建过程中,提出Gauss-Occupied (G-O)数据扩充方法以解决无线指纹数据库样本容量小的局限,大幅度降低人工采集的成本,进一步提高样本空间的表征范围。试验结果表明:提出的模型不仅能够有效提高无线定位系统的平均定位精度与抗噪能力,而且能够明显降低定位过程中出现的单点最大误差。

关键词: 无线指纹定位, 数据集扩充, 人工采集, 深度神经网络, 集成学习

Abstract:

Because of the low fault tolerance and weak anti-noise ability of fingerprint database in traditional wireless positioning model, an ensemble deep neural network wireless positioning method based on data fusion was proposed. This method could effectively overcome the interference caused by abnormal samples and noisy data on the wireless positioning system by sampling from the original fingerprint database randomly to generate train data for each base learner. During the process of fingerprint database construction, the Gauss-Occupied (G-O) data expansion method was proposed to solve the limitation of the small sample size of the wireless fingerprint database and decrease the cost of manual acquisition sharply, which increased the scope of the sample′s characterization. The results of the experiment showed that the proposed ensemble deep neural network wireless positioning model could not only improve the average positioning accuracy and the anti-noise ability of the wireless positioning system, but also reduce the maximum single point error in the positioning process.

Key words: wireless fingerprint positioning, data set expansion, artificial collection, deep neural network, ensemble learning

中图分类号: 

  • TP391

图1

无线信号随时间变化结果"

图2

高斯噪声信号"

图3

数据集扩充流程"

图4

深度神经网络基学习器"

图5

集成深度神经网络训练模型"

图6

某学生宿舍楼二层楼道平面布局图"

表1

G-O数据集扩充方法平均定位误差试验对比"

m
数据集 Error1 Error2
Testset1 0.401 0.513
Testset2 0.672 0.768
Testset3 0.614 0.756
Testset4 0.674 0.848

图7

平均定位误差随基学习器个数变化的结果"

图8

基学习器与集成学习器的平均定位误差对比结果"

表2

单一深度神经网络与集成深度神经网络平均定位误差试验对比"

m
模型 Testset1 Testset2 Testset3 Testset4
GO_DNN_A 0.390 0.652 0.628 0.663
GO_DNN_B 0.403 0.719 0.612 0.717
GO_DNN_C 0.396 0.688 0.601 0.694
GO_DNN_D 0.385 0.669 0.595 0.678
Ensemble 0.329 0.563 0.516 0.552

表3

单一深度神经网络与集成深度神经网络最大定位误差试验对比"

m
模型 Testset1 Testset2 Testset3 Testset4
GO_DNN_A 1.944 2.170 1.912 1.970
GO_DNN_B 1.855 1.906 2.330 2.653
GO_DNN_C 1.799 1.929 2.450 2.412
GO_DNN_D 2.044 1.891 2.184 2.182
Ensemble 1.610 1.654 1.500 1.556

表4

各算法平均定位误差试验对比"

m
模型 Testset1 Testset2 Testset3 Testset4
KNN 1.974 2.648 2.437 2.921
SVM 1.812 2.437 2.159 2.766
Ensemble 0.329 0.563 0.516 0.552

表5

各算法最大定位误差试验对比"

m
模型 Testset1 Testset2 Testset3 Testset4
KNN 5.725 6.177 5.870 7.474
SVM 4.874 4.946 4.324 5.489
Ensemble 1.610 1.654 1.500 1.556
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