Abstract

Pedestrian dead reckoning (PDR) is widely used for indoor localisation. Its principle is to recursively update the location of the pedestrian by using step length and step heading. A common method to estimate the heading in PDR is to use magnetometer measurements. However, unlike outdoor environments, the Earth's magnetic field is strongly perturbed inside buildings making the magnetometer measurements unreliable for heading estimation. This paper presents a new method to reduce heading estimation errors when magnetometers are used. The method consists of two components. The first component uses a machine learning algorithm to detect whether a heading estimate is within a specific error margin. Only heading estimates within the error margin are retained and passed to the second component, while the other estimates are discarded. The second component uses data fusion to average the …

Date

October 27, 2014

Authors

Marzieh Jalal Abadi, Luca Luceri, Mahbub Hassan, Chun Tung Chou, Monica Nicoli

Conference

2014 International Conference on Indoor Positioning and Indoor Navigation (IPIN)

Pages

554-563

Publisher

IEEE