Assessment of a GNSS/INS/Wi-Fi Tight-Integration Method Using Support Vector Machine and Extended Kalman Filter

Wi-Fi derived positions have been used in the past few years as a complementary source of positioning information for GNSS and Inertial Systems (INS). Ubiquitous positioning that transitions from indoors to outdoors and vice-versa is currently a hot topic of research. In this context, this study aims to analyze the potential of directional antennas sequentially tracking Wi-Fi signals on the 11 channels around the 2.4 GHz frequency in order to serve as an integrated signal for GNSS and INS positioning. Considering, as an example, a single point positioning (SPP) strategy coupled with an INS, the use of directional antennas can be beneficial in order to provide absolute directions of travel by the means of a Support Vector Machine (SVM) lane matching. In order to test the given hypothesis, real-world experiments were performed in areas with and without obstruction in an urban environment. Using a post-processed, smoothed in both forward and backward modes, and finally edited post-processed kinematic (RTK) solution as a reference, the solution integrating SPP GNSS, INS and Wi-Fi was assessed in terms of accuracy. Preliminary results show that such a combination of the directional antennas along with GNSS and INS and their respective SVM and EKF filters, can provide sub-meter accuracy at all times without the need of precise orbits or differential corrections, increasing solution availability, reliability and accuracy on a scalable and cost-effective way.