A Semi-Simulated RSS Fingerprint Construction for Indoor Wi-Fi Positioning
Fingerprinting-based Wi-Fi positioning has increased in popularity due to its existing infrastructure and wide coverage. However, in the offline phase of fingerprinting positioning, the construction and maintenance of a Received Signal Strength (<i>RSS</i>) fingerprint database yield hig...
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doaj-ddcd1bc5d82442e6ab738e267af193642020-11-25T03:47:00ZengMDPI AGElectronics2079-92922020-09-0191568156810.3390/electronics9101568A Semi-Simulated RSS Fingerprint Construction for Indoor Wi-Fi PositioningYuan Yang0Peng Dai1Haoqian Huang2Manyi Wang3Yujin Kuang4Key Laboratory of Micro-Inertial Instrument and Advanced Navigation Technology, Ministry of Education, School of Instrument Science and Engineering, Southeast University, Nanjing 210096, ChinaKey Laboratory of Micro-Inertial Instrument and Advanced Navigation Technology, Ministry of Education, School of Instrument Science and Engineering, Southeast University, Nanjing 210096, ChinaCollege of Energy and Electrical Engineering, Hohai University, Nanjing 210096, ChinaSchool of Mechanical Engineering, Nanjing University of Science and Technology, Nanjing 210096, ChinaKey Laboratory of Micro-Inertial Instrument and Advanced Navigation Technology, Ministry of Education, School of Instrument Science and Engineering, Southeast University, Nanjing 210096, ChinaFingerprinting-based Wi-Fi positioning has increased in popularity due to its existing infrastructure and wide coverage. However, in the offline phase of fingerprinting positioning, the construction and maintenance of a Received Signal Strength (<i>RSS</i>) fingerprint database yield high labor. Moreover, the sparsity and stability of <i>RSS</i> fingerprinting datasets can be the most dominant error sources. This work proposes a minimally Semi-simulated <i>RSS</i> Fingerprinting (SS-RSS) method to generate and maintain dense fingerprints from real spatially coarse <i>RSS</i> acquisitions. This work simulates dense fingerprints exploring the cosine similarity of the directions to Wi-Fi access points (APs), rather than only using either a log-distance path-loss model, a quadratic polynomial fitting, or a spatial interpolation method. Real-world experiment results indicate that the semi-simulated method performs better than the coarse fingerprints and close to the real dense fingerprints. Particularly, the model of <i>RSS</i> measurements, the ratio of the simulated fingerprints to all fingerprints, and a two dimensions (2D) spatial distribution have been analyzed. The average positioning accuracy achieves up to 1.01 m with 66.6% of the semi-simulation ratio. The SS-RSS method offers a solution for coarse fingerprint-based positioning to perform a fine resolution without a time-consuming site-survey.https://www.mdpi.com/2079-9292/9/10/1568indoor positioningWi-Fi fingerprintingsemi-simulated <i>RSS</i>cosine similarity |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Yuan Yang Peng Dai Haoqian Huang Manyi Wang Yujin Kuang |
spellingShingle |
Yuan Yang Peng Dai Haoqian Huang Manyi Wang Yujin Kuang A Semi-Simulated RSS Fingerprint Construction for Indoor Wi-Fi Positioning Electronics indoor positioning Wi-Fi fingerprinting semi-simulated <i>RSS</i> cosine similarity |
author_facet |
Yuan Yang Peng Dai Haoqian Huang Manyi Wang Yujin Kuang |
author_sort |
Yuan Yang |
title |
A Semi-Simulated RSS Fingerprint Construction for Indoor Wi-Fi Positioning |
title_short |
A Semi-Simulated RSS Fingerprint Construction for Indoor Wi-Fi Positioning |
title_full |
A Semi-Simulated RSS Fingerprint Construction for Indoor Wi-Fi Positioning |
title_fullStr |
A Semi-Simulated RSS Fingerprint Construction for Indoor Wi-Fi Positioning |
title_full_unstemmed |
A Semi-Simulated RSS Fingerprint Construction for Indoor Wi-Fi Positioning |
title_sort |
semi-simulated rss fingerprint construction for indoor wi-fi positioning |
publisher |
MDPI AG |
series |
Electronics |
issn |
2079-9292 |
publishDate |
2020-09-01 |
description |
Fingerprinting-based Wi-Fi positioning has increased in popularity due to its existing infrastructure and wide coverage. However, in the offline phase of fingerprinting positioning, the construction and maintenance of a Received Signal Strength (<i>RSS</i>) fingerprint database yield high labor. Moreover, the sparsity and stability of <i>RSS</i> fingerprinting datasets can be the most dominant error sources. This work proposes a minimally Semi-simulated <i>RSS</i> Fingerprinting (SS-RSS) method to generate and maintain dense fingerprints from real spatially coarse <i>RSS</i> acquisitions. This work simulates dense fingerprints exploring the cosine similarity of the directions to Wi-Fi access points (APs), rather than only using either a log-distance path-loss model, a quadratic polynomial fitting, or a spatial interpolation method. Real-world experiment results indicate that the semi-simulated method performs better than the coarse fingerprints and close to the real dense fingerprints. Particularly, the model of <i>RSS</i> measurements, the ratio of the simulated fingerprints to all fingerprints, and a two dimensions (2D) spatial distribution have been analyzed. The average positioning accuracy achieves up to 1.01 m with 66.6% of the semi-simulation ratio. The SS-RSS method offers a solution for coarse fingerprint-based positioning to perform a fine resolution without a time-consuming site-survey. |
topic |
indoor positioning Wi-Fi fingerprinting semi-simulated <i>RSS</i> cosine similarity |
url |
https://www.mdpi.com/2079-9292/9/10/1568 |
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