  Radio signal-based (indoor) localisation technique is important for IoT
applications such as smart factory and warehouse. Through machine learning,
especially neural networks methods, more accurate mapping from signal features
to target positions can be achieved. However, different radio protocols, such
as WiFi, Bluetooth, etc., have different features in the transmitted signals
that can be exploited for localisation purposes. Also, neural networks methods
often rely on carefully configured models and extensive training processes to
obtain satisfactory performance in individual localisation scenarios. The above
poses a major challenge in the process of determining neural network model
structure, or hyperparameters, as well as the selection of training features
from the available data. This paper proposes a neural network model
hyperparameter tuning and training method based on Bayesian optimisation.
Adaptive selection of model hyperparameters and training features can be
realised with minimal need for manual model training design. With the proposed
technique, the training process is optimised in a more automatic and efficient
way, enhancing the applicability of neural networks in localisation.
