Smartphone-based indoor localization using wi-fi fine timing measurement

As the number of smartphone users exploded, the demand for Location-Based Service (LBS) has increased. It is important for the LBS to specify the user location by utilizing the sensor built in the smartphone. Unlike outdoor localization, which can employ GPS, there are many challenging issues in indoor localization including non-line-of-sight (NLOS) and multipath effect. In our paper, we focus on Wi-Fi Fine Timing Measurement (FTM) which is a new function of the Android Pie Operating System (OS). We propose line-of-sight (LOS) identification algorithms applicable to Wi-Fi FTM and apply these algorithms to indoor localization based on multilateration methods. We utilize a hypothesis test framework and Support Vector Machine (SVM) to identify LOS signals. We divide LOS/NLOS signals as low and high-quality signals according to the degree of multipath error. We achieve high-quality signals identification rate of 92.4% on average in the sample size 99 and of 78.3% on average in the sample size 29. Therefore, we obtain a 24.4% localization performance improvement compared to the perfect LOS detector by using only high-quality signals to localization.