Knowledge-based error detection and correction method of a Multi-sensor Multi-network positioning platform for pedestrian indoor navigation

Abstract

For pedestrian indoor navigation, an accurate 2D/3D position is a premise for any further processing. Currently, indoor navigation is a challenging task for standalone GNSS technology. FGI has integrated self-contained sensors with wireless locating technology to investigate a hybrid indoor positioning solution. However, the infrastructure indoors inflicts multiple disturbances to the positioning sensors. A knowledge-based error detection and correction method is applied to detect and eliminate the occurring gross errors. Six modes of user dynamics are extracted from measurements of a barometer and an accelerometer, and such contexts can improve the positioning accuracy and enhance the user experience of the final navigation application.