Using prior measurements to improve probabilistic-based indoor localization methods

In this paper, we enhance a probabilistic method for determining the position of an unknown node in an indoor environment. Previous work shows that using a small subset of sensors with a probabilistic localization technique reduces the error in comparison to a large set of sensors. Because probability based models rely on a prior distribution for the unknown node, we propose that reference measurements performed at the time of deployment are used to improve the distribution model. We show this new method reduces the estimated location error and increases the likelihood of selecting the correct room.