A Kalman filter approach to reduce position error for pedestrian applications in areas of bad GPS reception

The number of GPS-enabled devices are growing rapidly. A large segment of the growth is coupled to the growth of smartphones. Some location-based applications are relatively simple, requiring only a rough position estimate. Other applications provide services where the usefulness of the application is directly connected to the accuracy of GPS positioning. Lifelogging, fitness, and navigation are some types of applications where precise location estimation greatly benefits the user. The GPS technology is available world wide and 24 hours per day. Its accuracy is not uniform, varying over time of day and place. Buildings can reflect or block the signal, the atmosphere delays it, satellite clock and orbit errors introduce bias. These are some of the error sources affecting GPS positioning. Many applications with need of high accuracy are used in everyday life. Users will eventually venture into areas unsuitable for GPS positioning. In these situations, these applications may not function sufficiently well. In this thesis, a data fusion method called the Kalman filter is evaluated as a means of improving the GPS positioning. A simple motion model is employed, tracking the position and velocity. The motion model utilizes sensors commonly available in a modern day smartphone. The Kalman filter will be evaluated through comparison to the raw data and a simple moving average filter. The results show that the Kalman filter is able to significantly reduce the variance compared to the raw data, but not significantly lower than the moving average filter.