Position error and entropy of probabilistic Wi Fi fingerprinting in the UJIIndoorLoc dataset
The accuracy of a positioning system is usually expressed as its average position error in an experiment. However, when the ground truth is no longer available, it would still be useful to know the reliability of a position estimate based on a single measurement. To obtain a reliability metric, we hypothesize that there is a relation between the uncertainty in a position's posterior probability distribution, expressed as its conditional entropy, and the position error of the position that is derived from this distribution. In this paper, we present the correlation between these two metrics as calculated for the UJIIndoorLoc Wi Fi fingerprinting dataset, using a new probabilistic sensor model. We found that there is no significant correlation between the conditional entropy and the position error. However, we learned that our sensor model is usually very certain in the dataset, and saw that the suggestion of a correlation improves when we increase the uncertainty by selecting a fixed, larger variance. Interestingly, the position error results improve as well.
2016 International Conference on Indoor Positioning and Indoor Navigation (IPIN)