Real-Time Indoor Localization for Smartphones Using Tensor-Generative Adversarial Nets

Abstract

High-accuracy location awareness in indoor environments is fundamentally important for mobile computing and mobile social networks. However, accurate radio frequency (RF) fingerprint-based localization is challenging due to real-time response requirements, limited RF fingerprint samples, and limited device storage. In this article, we propose a tensor generative adversarial net (Tensor-GAN) scheme for real-time indoor localization, which achieves improvements in terms of localization accuracy and storage consumption. First, with verification on real-world fingerprint data set, we model RF fingerprints as a 3-D low-tubal-rank tensor to effectively capture the multidimensional latent structures. Second, we propose a novel Tensor-GAN that is a three-player game among a regressor, a generator, and a discriminator. We design a tensor completion algorithm for the tubal-sampling pattern as the generator that produces new RF fingerprints as training samples, and the regressor estimates locations for RF fingerprints. Finally, on real-world fingerprint data set, we show that the proposed Tensor-GAN scheme improves localization accuracy from 0.42 m (state-of-the-art methods kNN, DeepFi, and AutoEncoder) to 0.19 m for 80% of 1639 random testing points. Moreover, we implement a prototype Tensor-GAN that is downloaded as an Android smartphone App, which has a relatively small memory footprint, i.e., 57 KB.