Accurate and Direct GNSS/PDR Integration Using Extended Kalman Filter for Pedestrian Smartphone Navigation

According to well-described literature concerning the work history of multipath mitigation in the global navigation satellite systems (GNSS), multipath is still the most dominant factor in a challenging environment. There are unperturbed harsh circumstances where GNSS signals cannot reach and smartphone navigation is not possible. The main objective of this research is to find an accurate solution for pedestrian smartphone navigation in a multipath environment. Experiments are done with micro-electro-mechanical system (MEMS) sensors mounted on a smartphone, and no extra hardware is needed. The latest Android smartphone is used to log the data files of GNSS and MEMS sensors. This scheme has been classified in the synopsis, and a rectangular route with three perpendicular turns has been selected for a pedestrian walk. The data is preprocessed using a low pass filter to remove high-frequency noise and smooth the signal. The description of accumulative error produced by the heading and step size estimation has been reduced by implementing the indices of mean cumulative heading error and cumulative step length error, respectively. In the end, the suboptimal extended Kalman filter algorithm is used to fuse the data of GNSS and pedestrian dead reckoning (PDR) for final results. In this paper, we try to give a technique to provide accurate pedestrian smartphone navigation. The fusion results show that the prospective method explores the possibility to use smartphone navigation in any case when GNSS or PDR information is not available. Substantial simulations are implemented and corroborate that the schemed method is sturdier to use in a harsh environments. The aim is to achieve high-level accuracy with an ultra-low-cost solution.