  The development of highly accurate deep learning methods for indoor
localization is often hindered by the unavailability of sufficient data
measurements in the desired environment to perform model training. To overcome
the challenge of collecting costly measurements, this paper proposes a
cross-environment approach that compensates for insufficient labelled
measurements via a joint semi-supervised and transfer learning technique to
transfer, in an appropriate manner, the model obtained from a rich-data
environment to the desired environment for which data is limited. This is
achieved via a sequence of operations that exploit the similarity across
environments to enhance unlabelled data model training of the desired
environment. Numerical experiments demonstrate that the proposed
cross-environment approach outperforms the conventional method, convolutional
neural network (CNN), with a significant increase in localization accuracy, up
to 43%. Moreover, with only 40% data measurements, the proposed
cross-environment approach compensates for data inadequacy and replicates the
localization accuracy of the conventional method, CNN, which uses 75% data
measurements.
