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Localization technology is important for the development of indoorlocation-based services (LBS). Global Positioning System (GPS) becomes invalidin indoor environments due to the non-line-of-sight issue, so it is urgent todevelop a real-time high-accuracy localization approach for smartphones.However, accurate localization is challenging due to issues such as real-timeresponse 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 aprior, our model applies artificial neural network with deep learning to trainnetwork classifiers and then gives out estimations. Then we propose a noveltensor-based super-resolution scheme using the generative adversarial network(GAN) that adopts sparse coding as the generator network and a residuallearning network as the discriminator. Further, we analyze the performance oftensor-GAN and implement a trace-based localization experiment, which achievesbetter performance. Compared to existing methods for smartphones indoorpositioning, that are energy-consuming and high demands on devices, TGAN cangive out an improved solution in localization accuracy, response time andimplementation complexity.

Tensor-Generative Adversarial Network with Two-dimensional Sparse Coding: Application to Real-time Indoor Localization