Journal of Shandong University(Engineering Science) ›› 2018, Vol. 48 ›› Issue (5): 95-102.doi: 10.6040/j.issn.1672-3961.0.2018.169

• Control Science & Engineering • Previous Articles     Next Articles

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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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

CLC Number: 

  • TP391

Fig.1

Results of wireless signals changed with time"

Fig.2

Gauss noise signal"

Fig.3

Data set expansion process"

Fig.4

Base learner of deep neural network"

Fig.5

Ensemble deep neural network training model"

Fig.6

Plane layout of second floor of a student′s dormitory building"

Table 1

Experimental comparison of average positioning error of G-O data set expansion method"

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

Fig.7

The average positioning error curves varying with the number of base learner"

Fig.8

Comparison of average positioning error between base learner and ensemble learner"

Table 2

Comparison of average positioning error between single deep neural network and ensemble deep neural network"

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

Table 3

Comparison of maximum positioning error between single deep neural network and ensemble deep neural network"

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

Table 4

Experimental comparison of average positioning error for each algorithm"

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

Table 5

Experimental comparison of maximum positioning error for each algorithm"

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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