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

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...

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Bibliographic Details
Published in:IEEE transaction on neural networks and learning systems 2021-08, Vol.32 (8), p.3433-3443
Main Authors: Liu, Xiao-Yang, Wang, Xiaodong
Format: Article
Language:English
Subjects:
Accuracy
Algorithms
Datasets
Fingerprinting
Fingerprints
Generators
Indoor environments
Localization
Mathematical analysis
Methyltestosterone
Mobile computing
Neural networks
Radio frequency
radio frequency (RF) fingerprint
Real time
Real-time indoor localization
Real-time systems
Smart phones
Smartphones
Social networks
Social organization
Tensile stress
tensor completion
tensor generative adversarial net (Tensor-GAN)
Tensors
Time response
tubal sampling
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Summary: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.
ISSN:2162-237X
2162-2388
DOI:10.1109/TNNLS.2020.3010724