Enhancement of Robustness and Precision of Indoor Positioning by Fusing Wifi Fingerprinting and Pdr Techniques
Muhammad Shahid Jabbar1, Ghulam Hussain2, Jundong Cho3, Sangmin Bae4
1Muhammad Shahid Jabbar, College of Information and Communication Engineering, Sungkyunkwan University, Suwon, Korea.
2Ghulam Hussain, College of Information and Communication Engineering, Sungkyunkwan University, Suwon, Korea.
3Jundong Cho, College of Information and Communication Engineering, Sungkyunkwan University, Suwon, Korea.
4Sangmin Bae, College of Information and Communication Engineering, Sungkyunkwan University, Suwon, Korea.
Manuscript received on 05 March 2019 | Revised Manuscript received on 12 March 2019 | Manuscript Published on 20 March 2019 | PP: 23-27 | Volume-8 Issue- 4S2 March 2019 | Retrieval Number: D1S0006028419/2019©BEIESP
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open-access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Fused approaches for enhancing robustness and precision of indoor positioning using pedestrian dead reckoning (PDR) and KNN (K-Nearest Neighbors) classifier based WiFi fingerprinting were proposed. The proposed machine learning approaches employed the rough position estimate by PDR as a pre-sorter of training vectors of KNN classifier and help improve precision by overcoming fluctuating radio signal and furthermore robustness in serious radio signal distortion by the undesired malfunction of WiFi signal sources. The experiment in real space showed significant improvement in both precision and robustness.
Keywords: Fusion with Fingerprinting and PDR; K-Nearest Neighbors Algorithm; Machine Learning; Pedestrian Dead Reckoning (PDR) Algorithm; Robust-ness and Precision in indoor Positioning; WiFi Fingerprinting Indoor Positioning.
Scope of the Article: Communication