  Precise indoor localization is one of the key requirements for fifth
Generation (5G) and beyond, concerning various wireless communication systems,
whose applications span different vertical sectors. Although many highly
accurate methods based on signal fingerprints have been lately proposed for
localization, their vast majority faces the problem of degrading performance
when deployed in indoor systems, where the propagation environment changes
rapidly. In order to address this issue, the crowdsourcing approach has been
adopted, according to which the fingerprints are frequently updated in the
respective database via user reporting. However, the late crowdsourcing
techniques require precise indoor floor plans and fail to provide satisfactory
accuracy. In this paper, we propose a low-complexity self-calibrating indoor
crowdsourcing localization system that combines historical with frequently
updated fingerprints for high precision user positioning. We present a
multi-kernel transfer learning approach which exploits the inner relationship
between the original and updated channel measurements. Our indoor laboratory
experimental results with the proposed approach and using Nexus 5 smartphones
at 2.4GHz with 20MHz bandwidth have shown the feasibility of about one meter
level accuracy with a reasonable fingerprint update overhead.
