MOCLoc: Emerging Online Collaborative Localization Enhanced by Multidimensional Scaling

In human activity recognition, it is important to first recognize the position of a user. Although numerous fingerprinting schemes have been researched for indoor localization, they work in a stand-alone mode without considering the potentials of exploiting multiple requesting users’ online fingerprints. In this paper, we propose an emerging online collaborative localization paradigm to serve the scenarios of multiple users requesting localizations at the same time, which enables a kind of collective calibration by exploiting latent relations in between requesting users in both the signal space and physical space. The proposed scheme is called MOCLoc, which first applies the multidimensional scaling (MDS) technique to compute online users’ virtual locations based on their online fingerprints. A new virtual location credibility is next computed for each selected reference point (RP) based on the similarity between its virtual distances and physical distances to other online users. We also compute a collaborative credibility and signal credibility and fuse all three kinds of credibilities to compute a new RP ranking weight that is used to output the user final location. Furthermore, we propose to include a device calibration module based on the mean-subtraction fingerprint transformation to differentiate the collaborative calibration process for heterogeneous online devices. Experiments on field measurements validate the effectiveness of the proposed MOCLoc scheme in terms of further reduced localization errors.