  This paper studies a WiFi indoor localisation technique based on using a deep
learning model and its transfer strategies. We take CSI packets collected via
the WiFi standard channel sounding as the training dataset and verify the CNN
model on the subsets collected in three experimental environments. We achieve a
localisation accuracy of 46.55 cm in an ideal $(6.5m \times 2.5m)$ office with
no obstacles, 58.30 cm in an office with obstacles, and 102.8 cm in a sports
hall $(40 \times 35m)$. Then, we evaluate the transfer ability of the proposed
model to different environments. The experimental results show that, for a
trained localisation model, feature extraction layers can be directly
transferred to other models and only the fully connected layers need to be
retrained to achieve the same baseline accuracy with non-transferred base
models. This can save 60% of the training parameters and reduce the training
time by more than half. Finally, an ablation study of the training dataset
shows that, in both office and sport hall scenarios, after reusing the feature
extraction layers of the base model, only 55% of the training data is required
to obtain the models' accuracy similar to the base models.
