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The development of highly accurate deep learning methods for indoorlocalization is often hindered by the unavailability of sufficient datameasurements in the desired environment to perform model training. To overcomethe challenge of collecting costly measurements, this paper proposes across-environment approach that compensates for insufficient labelledmeasurements via a joint semi-supervised and transfer learning technique totransfer, in an appropriate manner, the model obtained from a rich-dataenvironment to the desired environment for which data is limited. This isachieved via a sequence of operations that exploit the similarity acrossenvironments to enhance unlabelled data model training of the desiredenvironment. Numerical experiments demonstrate that the proposedcross-environment approach outperforms the conventional method, convolutionalneural network (CNN), with a significant increase in localization accuracy, upto 43%. Moreover, with only 40% data measurements, the proposedcross-environment approach compensates for data inadequacy and replicates thelocalization accuracy of the conventional method, CNN, which uses 75% datameasurements.

Indoor Localization Under Limited Measurements: A Cross-Environment Joint Semi-Supervised and Transfer Learning Approach