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IoT-Aided Fingerprint Indoor Positioning Using Support Vector Classification
- Authors
- Wei, Yiqiao; Hwang, Seung-Hoon; Lee, Sang-Moon
- Issue Date
- 16-Nov-2018
- Publisher
- IEEE
- Keywords
- Indoor positioning; Received Signal Strength; Fingerprint; Support vector machine; IoT
- Citation
- 2018 INTERNATIONAL CONFERENCE ON INFORMATION AND COMMUNICATION TECHNOLOGY CONVERGENCE (ICTC), pp 973 - 975
- Pages
- 3
- Indexed
- SCOPUS
- Journal Title
- 2018 INTERNATIONAL CONFERENCE ON INFORMATION AND COMMUNICATION TECHNOLOGY CONVERGENCE (ICTC)
- Start Page
- 973
- End Page
- 975
- ISSN
- 2162-1233
- Abstract
- Wi-Fi based fingerprint indoor positioning technology is known as one of the most popular indoor positioning technologies. In this work, an internet of things (IoT) aided fingerprint indoor positioning system using support vector machine classifier has been proposed. The support vector classification with kernel tricks is introduced to accomplish multi-classes classification problem in fingerprint indoor positioning. Three kinds of kernel functions are investigated and compared based on results of the experiment performed in a real indoor environment. The results show support vector classifier with Gaussian RBF kernel function has highest positioning accuracy.
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Related Researcher
- Hwang, Seung Hoon
- College of Engineering (Department of Electronics and Electrical Engineering)