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GPS/Wi-Fi integration for challenging environments using raw GNSS data

University of British Columbia

Over the last few years, the Global Positioning System (GPS) has become the most popular technology for positioning. In 2016 Google announced that raw GPS measurements are available in smartphones. This led to obtaining pseudoranges, Doppler, carrier-phase measurements and signal-to-noise ratio (SNR) all in one file. This opened a new era in the GPS world. But GPS has its weaknesses. It only works with a clear view of the sky. Therefore, it doesn’t work when indoors due to multipath and signal blockage. Indoors, Wi-Fi has proven to be a reliable positioning system. So, it is desirable to combine these two systems in situations where GPS does not provide an accurate position due to few visible satellites or when due to signal blockage GPS cannot provide a position. In this thesis, a tightly integrated GPS/Wi-Fi Kalman filter algorithm is introduced using raw GPS measurements to provide a position when GPS satellite signals are very weak or blocked. The performance of the GPS/Wi-Fi integration method is tested in a signal blockage scenario where a pedestrian walks from outside a building, to the inside, and then walks outside again. The results show that the integrated GPS/Wi-Fi solution with respect to the reference trajectory has root mean square (RMS) of 83.5m and 43.1m east and north respectively which is lower than the RMS of the GPS-only solution of 175.1m and 83.3m east and north respectively.

Text

2021-02-08

Attribution-NonCommercial-NoDerivatives 4.0 International

http://hdl.handle.net/2429/77253

Electrical Engineering

University of British Columbia

UBCO

http://creativecommons.org/licenses/by-nc-nd/4.0/