  Localization technology is important for the development of indoor
location-based services (LBS). Global Positioning System (GPS) becomes invalid
in indoor environments due to the non-line-of-sight issue, so it is urgent to
develop a real-time high-accuracy localization approach for smartphones.
However, accurate localization is challenging due to issues such as real-time
response requirements, limited fingerprint samples and mobile device storage.
To address these problems, we propose a novel deep learning architecture:
Tensor-Generative Adversarial Network (TGAN).
  We first introduce a transform-based 3D tensor to model fingerprint samples.
Instead of those passive methods that construct a fingerprint database as a
prior, our model applies artificial neural network with deep learning to train
network classifiers and then gives out estimations. Then we propose a novel
tensor-based super-resolution scheme using the generative adversarial network
(GAN) that adopts sparse coding as the generator network and a residual
learning network as the discriminator. Further, we analyze the performance of
tensor-GAN and implement a trace-based localization experiment, which achieves
better performance. Compared to existing methods for smartphones indoor
positioning, that are energy-consuming and high demands on devices, TGAN can
give out an improved solution in localization accuracy, response time and
implementation complexity.
