The GPS tracking in sprint orienteering is often a poor supplement to the viewer experience during events taking place in urban areas because of multipath effects. Since the GPS tracking of runners is an important means to making the sport more spectator friendly, it is of interest to make it more accurate. In this thesis project, the information provided by the map of a competition is fused with the GPS tracker position measurements and punch time data in a particle filter to create estimates of the runner trajectories. The map is used to create constraints and to predict motion of runners, as well as to create a model of the GPS reliability depending on map position. A simple observation model is implemented, using the map to decide if a GPS measurement is reliable or not depending on the distance to the closest building. A rather complex motion model is developed to predict the runner motion within the constraints given by the map. The results show that given certain conditions the improvements are vast compared to the traditional GPS tracking. The estimates are bound to possible routes, and they are often very good given that alternative route choices are easily separable. It is however principally difficult to generally improve the tracking using this method. Better measurements or observation models are needed in order to receive a fully satisfying tracking.