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An Indoor and Outdoor Positioning Using a Hybrid of Support Vector Machine and Deep Neural Network Algorithms
Joint Authors
Adege, Abebe Belay
Lin, Hsin-Piao
Tarekegn, Getaneh Berie
Munaye, Yirga Yayeh
Yen, Lei
Source
Issue
Vol. 2018, Issue 2018 (31 Dec. 2018), pp.1-12, 12 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2018-12-16
Country of Publication
Egypt
No. of Pages
12
Main Subjects
Abstract EN
Indoor and outdoor positioning lets to offer universal location services in industry and academia.
Wi-Fi and Global Positioning System (GPS) are the promising technologies for indoor and outdoor positioning, respectively.
However, Wi-Fi-based positioning is less accurate due to the vigorous changes of environments and shadowing effects.
GPS-based positioning is also characterized by much cost, highly susceptible to the physical layouts of equipment, power-hungry, and sensitive to occlusion.
In this paper, we propose a hybrid of support vector machine (SVM) and deep neural network (DNN) to develop scalable and accurate positioning in Wi-Fi-based indoor and outdoor environments.
In the positioning processes, we primarily construct real datasets from indoor and outdoor Wi-Fi-based environments.
Secondly, we apply linear discriminate analysis (LDA) to construct a projected vector that uses to reduce features without affecting information contents.
Thirdly, we construct a model for positioning through the integration of SVM and DNN.
Fourthly, we use online datasets from unknown locations and check the missed radio signal strength (RSS) values using the feed-forward neural network (FFNN) algorithm to fill the missed values.
Fifthly, we project the online data through an LDA-based projected vector.
Finally, we test the positioning accuracies and scalabilities of a model created from a hybrid of SVM and DNN.
The whole processes are implemented using Python 3.6 programming language in the TensorFlow framework.
The proposed method provides accurate and scalable positioning services in different scenarios.
The results also show that our proposed approach can provide scalable positioning, and 100% of the estimation accuracies are with errors less than 1 m and 1.9 m for indoor and outdoor positioning, respectively.
American Psychological Association (APA)
Adege, Abebe Belay& Lin, Hsin-Piao& Tarekegn, Getaneh Berie& Munaye, Yirga Yayeh& Yen, Lei. 2018. An Indoor and Outdoor Positioning Using a Hybrid of Support Vector Machine and Deep Neural Network Algorithms. Journal of Sensors،Vol. 2018, no. 2018, pp.1-12.
https://search.emarefa.net/detail/BIM-1200682
Modern Language Association (MLA)
Adege, Abebe Belay…[et al.]. An Indoor and Outdoor Positioning Using a Hybrid of Support Vector Machine and Deep Neural Network Algorithms. Journal of Sensors No. 2018 (2018), pp.1-12.
https://search.emarefa.net/detail/BIM-1200682
American Medical Association (AMA)
Adege, Abebe Belay& Lin, Hsin-Piao& Tarekegn, Getaneh Berie& Munaye, Yirga Yayeh& Yen, Lei. An Indoor and Outdoor Positioning Using a Hybrid of Support Vector Machine and Deep Neural Network Algorithms. Journal of Sensors. 2018. Vol. 2018, no. 2018, pp.1-12.
https://search.emarefa.net/detail/BIM-1200682
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1200682