This paper introduces our work in the framework of Track 3 of the IPIN 2016 Indoor Localization Competition, which addresses the smartphone-based tracking problem in an offline manner. Our approach splits the path-reconstruction into several smaller tasks, including building identification, floor identification, user direction and speed inference. For each task, a specific set of data from the provided log data is used. Evaluation is carried out using a cross validation scheme. To produce the robustness again noisy data, we combine several approaches into one on the basis of their testing results. By testing on the provided training data, we have a good accuracy on building and floor identification. For the task of tracking the user's position within the floor, the result is 10m at 3rd-quarter distance error after 3 minutes of walking.