Indoor Localization with Wi-Fi Fine Timing Measurements Through Range Filtering and Fingerprinting Methods Sami Huilla * , Chrysanthos Pepi † , Michalis Antoniou † , Christos Laoudias † , Seppo Horsmanheimo * , Sergio Lembo * , Matti Laukkanen * and Georgios Ellinas † * VTT Technical Research Centre of Finland Ltd, Espoo, Finland † KIOS Research and Innovation Center of Excellence, University of Cyprus, Nicosia, Cyprus Email: {Sami.Huilla, Seppo.Horsmanheimo, Sergio.Lembo, Matti.Laukkanen}@vtt.fi, {pepi.chrysanthos, antoniou.a.michalis, laoudias, gellinas}@ucy.ac.cy Abstract—Wi-Fi technology has been thoroughly studied for indoor localization. This is mainly due to the existing infrastruc- ture inside buildings for wireless connectivity and the uptake of mobile devices where Wi-Fi location-dependent measurements, e.g., timing and signal strength readings, are readily available to determine the user location. To enhance the accuracy of Wi-Fi solutions, a two-way ranging approach was recently introduced into the IEEE 802.11 standard for the provision of Fine Timing Measurements (FTM). Such measurements enable a more reliable estimation of the distance between FTM-capable Wi-Fi access points and user-carried devices; thus, promising to deliver meter- level location accuracy. In this work, we propose two novel solu- tions that leverage FTM and follow different approaches, which have not been investigated in the literature. The first solution is based on an Unscented Kalman Filter (UKF) algorithm to process FTM ranging measurements, while the second solution relies on an FTM fingerprinting method. Experimental results using real- life data collected in a typical office environment demonstrate the effectiveness of both solutions, while the FTM fingerprinting approach demonstrated 1.12m and 2.13m localization errors for the 67-th and 95-th percentiles, respectively. This is a two to three times improvement over the traditional Wi-Fi signal strength fingerprinting approach and the UKF ranging algorithm. Index Terms—Indoor localization, Wi-Fi , Fine Timing Mea- surements, Unscented Kalman Filter, fingerprinting. I. I NTRODUCTION The ever-increasing demand for location awareness and accurate positioning information in GPS-deprived indoor en- vironments, such as airports and shopping malls, is driving in- novation in the area of localization technologies. The ubiquity of Wi-Fi infrastructure inside buildings, along with the need for mobile Internet connectivity and the market penetration of smartphones equipped with Wi-Fi circuitry, inevitably led to the use of Wi-Fi as a viable solution for the provision of reliable location information [1]. While Wi-Fi Received Signal Strength (RSS) measurements have been widely explored for This work is done jointly in the LuxTurrim5G+ project funded by Business Finland (https://www.luxturrim5g.com/) and the KIOS CoE project supported by the European Union’s H2020 research and innovation programme under grant agreement No 739551 and from the Republic of Cyprus through the Di- rectorate General for European Programmes, Coordination and Development. smartphone-based indoor localization [2], recently Wi-Fi ven- dors are incorporating localization assisting technologies into their devices, both Wi-Fi Access Points (AP) and smartphones, to improve the location accuracy. Interestingly, one of the most promising technologies utilizes the IEEE 802.11-2016 standardized Fine Timing Measurement (FTM) protocol [3]. The FTM protocol allows a proprietary firmware, running in an IEEE 802.11-2016 Wi-Fi card, to derive the Round-Trip Time (RTT), between a Wi-Fi AP and a User Device (UD), e.g., commercial smartphone, and subsequently to compute the distance between those two devices. The RTT is com- puted based on the timestamp difference between a dedicated FTM transmitted message and its received acknowledgement message (ACK). The FTM process is initiated by a UD after sending an FTM request to an FTM-enabled Wi-Fi AP. The RTT reading is then converted to a ranging (i.e., distance) measurement between two communicating Wi-Fi devices. Subsequently, multiple ranging measurements from Wi-Fi APs in the vicinity can be used to determine the location of the UD, e.g., through standard multilateration positioning algorithms that localize the UD at the intersection of multiple circles each one centered at the location of the corresponding Wi-Fi AP [1]. Considering the improvements in clock resolution of IEEE 802.11-2016 Wi-Fi modules, the Wi-Fi Alliance claims that meter-level ranging accuracy is feasible [4], [5]. Several existing works report high accuracy in FTM-based localization. In most cases, a calibration process is typically applied as a pre-processing step to alleviate the effect of Non- Line-Of-Sight (NLOS) conditions that introduce bias to the measured FTM ranges as opposed to Line-Of-Sight (LOS), while multilateration algorithms are then applied to localize the UD with calibrated FTM ranges. However, to achieve meter-level accuracy, FTM ranges need to be combined with smartphone sensory data (e.g., accelerometer) and/or Wi-Fi RSS measurements, and some times further processing is applied by means of Bayesian filtering approaches including the Kalman filter, Extended Kalman Filter (EKF), and parti- cle filter (see Section II for an extensive literature review). Nonetheless, the well-known Unscented Kalman Filter (UKF)