  Fingerprinting-based positioning, one of the promising indoor positioning
solutions, has been broadly explored owing to the pervasiveness of sensor-rich
mobile devices, the prosperity of opportunistically measurable
location-relevant signals and the progress of data-driven algorithms. One
critical challenge is to controland improve the quality of the reference
fingerprint map (RFM), which is built at the offline stage and applied for
online positioning. The key concept concerningthe quality control of the RFM is
updating the RFM according to the newly measured data. Though varies methods
have been proposed for adapting the RFM, they approach the problem by
introducing extra-positioning schemes (e.g. PDR orUGV) and directly adjust the
RFM without distinguishing whether critical changes have occurred. This paper
aims at proposing an extra-positioning-free solution by making full use of the
redundancy of measurable features. Loosely inspired by random sampling
consensus (RANSAC), arbitrarily sampled subset of features from the online
measurement are used for generating multi-resamples, which areused for
estimating the intermediate locations. In the way of resampling, it can
mitigate the impact of the changed features on positioning and enables to
retrieve accurate location estimation. The users location is robustly computed
by identifying the candidate locations from these intermediate ones using
modified Jaccardindex (MJI) and the feature-wise change belief is calculated
according to the world model of the RFM and the estimated variability of
features. In order to validate our proposed approach, two levels of
experimental analysis have been carried out. On the simulated dataset, the
average change detection accuracy is about 90%. Meanwhile, the improvement of
positioning accuracy within 2 m is about 20% by dropping out the features that
are detected as changed when performing positioning comparing to that of using
all measured features for location estimation. On the long-term collected
dataset, the average change detection accuracy is about 85%.
