Localization performance quantification by conditional entropy

The performance of a localization algorithm is usually expressed as its mean error distance. We argue that this assumes a unimodal distribution of the localization posterior, which is not always appropriate. We propose to additionally quantify the localization posterior distribution by its conditional entropy. This informs us of the uncertainty over the position after a measurement, which must be processed by the localization algorithm. Our example measurement model was ranked in the Evaluating Ambient Assisted Living competition, for which we present the results. Furthermore, we discuss the conditional entropy of our measurement model and two additional measurement models, based on the absolute difference distance and the Pompeiu-Hausdorff distance. We compare these results by using the UJIIndoorLoc database that was also used for the competition.