Abstract
Indoor positioning which uses signal strength values of Wi-Fi networks have become popular as these wireless networks often already exist and many mobile devices, such as smartphones or tablets, have built-in Wi-Fi cards. Usually fingerprinting is employed for positioning which achieves relatively low positioning accuracies on the several meter level. In the scope of this work two methods are presented which have the potential to improve the fingerprinting performance using long-time RSS observations at reference stations. Both methods employ the usage of at least three reference stations surrounding the area of interest on which signal strength observations are continuously performed during the whole measurement process. Thereby the first method uses a 2-D linear plane-interpolation for the deduction of real-time corrections. For that purpose, the measured signal strengths are reduced by the long-time measurements which are interpolated at the approximate position of the measuring point. In the second method the daily average of the long-time measurements is applied and the improvements of the measurements are calculated by the deviation from the daily average. For this method it is conceivable that a single reference station may be sufficient if it is located in the middle of the area of interest. Field tests were performed in an office building and are analyzed. The fingerprinting algorithms reached an averaged positioning accuracy of around 5 m in dependence on the used smartphone. The daily average improvements (DAI) method provided a better performance than the interpolation method which is highly influenced by the required approximate position of the user.
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References
Bai Y B, Wu S, Retscher G, Kealy A, Holden L, Tomko M, Borriak A, Hu B, Wu H R & Zhang K (2014) A New Method for Improving Wi-Fi Based Indoor Positioning Accuracy. Journal of Location-Based Services LBS, 8:3:135–147.
Ettlinger A F & Retscher G (2016) Positioning Using Ambient Magnetic Fields in Combination with Wi-Fi and RFID. 7th International Conference Indoor Positioning and Indoor Navigation IPIN 2016, October 4–6, Alcalá de Henares, Madrid, Spain.
Li B & Rizos C (2014) Editorial: Special Issue International Conference on Indoor Positioning and Navigation 2012, Part 2. Journal of Location Based Services, 8:1:1–2.
Huang H (2014): Post Hoc Indoor Localization Based on RSS Fingerprinting in WLAN. Master thesis, University of Massachusetts, U.S.A.
Kaemarungsi K (2005): Design of Indoor Positioning Systems Based on Location Fingerprinting Technique. PhD thesis, University of Pittsburgh, U.S.A.
Mok E & Retscher G (2007) Location Determination Using WiFi Fingerprinting Versus WiFi Trilateration. Journal of Location Based Services, 1:2:145–159.
Retscher G. (2007): Augmentation of Indoor Positioning Systems with a Barometric Pressure Sensor for Direct Altitude Determination in a Multi-storey Building. Journal of Cartography and Geographic Information Science (CaGIS), 34: 4:305–310.
Retscher G & Tatschl T (2016a) Differential Wi-Fi – A Novel Approach for Wi-Fi Positioning Using Lateration. FIG Working Week, May 2–6, Christchurch, New Zealand.
Retscher G & Tatschl T (2016b) Indoor Positioning Using Wi-Fi Lateration – Comparison of two Common Range Conversion Models with Two Novel Differential Approaches. IEEE Xplore, 2016 Ubiquitous Positioning Indoor Navigation and Location Based Service (UPINLBS), November 3–4, Shanghai, PR China.
Tatschl T (2016) Indoor Positionierung mit differenziellem WLAN. Master thesis, TU Wien, Vienna, Austria (in German).
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Retscher, G., Roth, F. (2017). Wi-Fi Fingerprinting with Reduced Signal Strength Observations from Long-Time Measurements. In: Gartner, G., Huang, H. (eds) Progress in Location-Based Services 2016. Lecture Notes in Geoinformation and Cartography(). Springer, Cham. https://doi.org/10.1007/978-3-319-47289-8_1
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DOI: https://doi.org/10.1007/978-3-319-47289-8_1
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