Map-Based Indoor Pedestrian Navigation Using an Auxiliary Particle Filter
AbstractIn this research, a non-infrastructure-based and low-cost indoor navigation method is proposed through the integration of smartphone built-in microelectromechanical systems (MEMS) sensors and indoor map information using an auxiliary particle filter (APF). A cascade structure Kalman particle filter algorithm is designed to reduce the computational burden and improve the estimation speed of the APF by decreasing its update frequency and the number of particles used in this research. In the lower filter (Kalman filter), zero velocity update and non-holonomic constraints are used to correct the error of the inertial navigation-derived solutions. The innovation of the design lies in the combination of upper filter (particle filter) map-matching and map-aiding methods to further constrain the navigation solutions. This proposed navigation method simplifies indoor positioning and makes it accessible to individual and group users, while guaranteeing the system’s accuracy. The availability and accuracy of the proposed algorithm are tested and validated through experiments in various practical scenarios. View Full-Text
Share & Cite This Article
Yu, C.; El-Sheimy, N.; Lan, H.; Liu, Z. Map-Based Indoor Pedestrian Navigation Using an Auxiliary Particle Filter. Micromachines 2017, 8, 225.
Yu C, El-Sheimy N, Lan H, Liu Z. Map-Based Indoor Pedestrian Navigation Using an Auxiliary Particle Filter. Micromachines. 2017; 8(7):225.Chicago/Turabian Style
Yu, Chunyang; El-Sheimy, Naser; Lan, Haiyu; Liu, Zhenbo. 2017. "Map-Based Indoor Pedestrian Navigation Using an Auxiliary Particle Filter." Micromachines 8, no. 7: 225.
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.