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Precise indoor localization is one of the key requirements for fifthGeneration (5G) and beyond, concerning various wireless communication systems,whose applications span different vertical sectors. Although many highlyaccurate methods based on signal fingerprints have been lately proposed forlocalization, their vast majority faces the problem of degrading performancewhen deployed in indoor systems, where the propagation environment changesrapidly. In order to address this issue, the crowdsourcing approach has beenadopted, according to which the fingerprints are frequently updated in therespective database via user reporting. However, the late crowdsourcingtechniques require precise indoor floor plans and fail to provide satisfactoryaccuracy. In this paper, we propose a low-complexity self-calibrating indoorcrowdsourcing localization system that combines historical with frequentlyupdated fingerprints for high precision user positioning. We present amulti-kernel transfer learning approach which exploits the inner relationshipbetween the original and updated channel measurements. Our indoor laboratoryexperimental results with the proposed approach and using Nexus 5 smartphonesat 2.4GHz with 20MHz bandwidth have shown the feasibility of about one meterlevel accuracy with a reasonable fingerprint update overhead.

Self-Calibrating Indoor Localization with Crowdsourcing Fingerprints and Transfer Learning