"An ensemble approach to deep-learning-based wireless indoor localizati" by Juthatip Wisanmongkol, Attaphongse Taparugssanagorn et al.
The authors investigate the use of deep learning in wireless indoor localization to address the shortcomings of the existing range-based (e.g. trilateration and triangulation) and range-free (e.g. fingerprinting) localization. Instead of relying on geometric models and hand-picked features, deep learning can automatically extract the relationship between the observed data and the target's location. Nevertheless, a deep neural network (DNN) model providing a satisfactory accuracy might perform differently when it is retrained in the deployment. To mitigate this issue, the authors propose a...
Source: uow.edu.au