While some indoor Location Based Services (LBSs), such as medical equipment location in hospitals or people location in museums, do not need to estimate the trajectory of devices at short time intervals, some others, such as people guidance, require a frequent estimation of the device position. When providing an LBS for the latter, motion models and the information provided from motion sensors are commonly used to reduce the error in the localization, but this information is not always available. In this paper, we propose an approach to estimate the position of a moving device using a topological radio-map designed for static WiFi localization in a previous work. This approach uses a Bayes filter that continuously estimates the most likely position of the device. This filter will have to deal with the low working frequency of the device and the uncertainty of the observation to provide an accurate and fast estimation. Experiments performed in a real multi-floor environment show that the system is able to correctly track the device position, reducing the mean localization error.