Multiple Systems Integration for Pedestrian Indoor Navigation
Numerous solutions to solve existing problems of pedestrian navigation have been proposed in the last decade by both industrial and academic researchers. However, to date, there are still major challenges for a pedestrian navigation system (PNS) to operate continuously, robustly, and seamlessly in all indoor/outdoor environments. Since the Global Navigation Satellite System (GNSS) is reliable under most outdoor environments, novel methods for pedestrian indoor navigation applications through integrating different navigation techniques and systems were proposed in this thesis. A PNS architecture based on a single device was first introduced, integrating the inertial navigation system (INS) mechanization, the pedestrian dead reckoning (PDR) mechanization, and the WiFi fingerprinting positioning method. Experimental evaluation showed that the proposed WiFi/PDR/INS algorithm not only tracks closest to the actual pedestrian walking trajectories but also provides the navigation results with good continuity. When multiple PNSs are used simultaneously by a specific user, novel information fusion methods for multiple PNSs integration to enhance the performance of each PNS are proposed. A nonlinear inequality distance constraint between any two PNSs was mathematically formulated. A novel filtering technique named the state-constrained Kalman filter (KF) was used to explore such a constraint information, further diminishing the positioning errors of each PNS. Two different approaches based on the state-constrained KF for solving the multiple PNSs integration problem were proposed. The first approach incorporates a soft constraint into a KF to enable the state estimate almost satisfies the constraint rather than strictly satisfies the constraint; the second approach is based on solving a Quadratic Programming (QP) problem to ensure that the state estimate should strictly satisfy the constraint. Simulation studies and field experiments were conducted to assess the two proposed approaches. The results showed that both approaches could well bound the navigation state errors compared with the unconstrained state estimate. However, the performance of the hard constraint approach was better than that of the soft constraint approach when a constraint’s nonlinearity level increased. It is indicated from this research that using motion sensor data from multiple mobile devices could provide more accurate navigation solutions for a pedestrian in all indoor/outdoor environments.
Lan, H. (2016). Multiple Systems Integration for Pedestrian Indoor Navigation (Doctoral thesis, University of Calgary, Calgary, Canada). Retrieved from https://prism.ucalgary.ca. doi:10.11575/PRISM/27034