  We propose an approach for fingerprinting-based positioning which reduces the
data requirements and computational complexity of the online positioning stage.
It is based on a segmentation of the entire region of interest into subregions,
identification of candidate subregions during the online-stage, and position
estimation using a preselected subset of relevant features. The subregion
selection uses a modified Jaccard index which quantifies the similarity between
the features observed by the user and those available within the reference
fingerprint map. The adaptive feature selection is achieved using an adaptive
forward-backward greedy search which determines a subset of features for each
subregion, relevant with respect to a given fingerprinting-based positioning
method. In an empirical study using signals of opportunity for fingerprinting
the proposed subregion and feature selection reduce the processing time during
the online-stage by a factor of about 10 while the positioning accuracy does
not deteriorate significantly. In fact, in one of the two study cases the 90th
percentile of the circular error increased by 7.5% while in the other study
case we even found a reduction of the corresponding circular error by 30%.
