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首页> 外文期刊>Neural Networks and Learning Systems, IEEE Transactions on >Real-Time Indoor Localization for Smartphones Using Tensor-Generative Adversarial Nets
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Real-Time Indoor Localization for Smartphones Using Tensor-Generative Adversarial Nets

机译：智能手机使用张量 - 生成对抗性网的实时室内定位

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摘要

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.
机译：室内环境中的高精度位置意识是对移动计算和移动社交网络的根本重要意义。然而，由于实时响应要求，有限的RF指纹样本和有限的设备存储，精确的射频（RF）指纹定位是具有挑战性的。在本文中，我们提出了一种用于实时内部定位的张量生成的对抗净（Tensor-GaN）方案，这实现了本地化精度和储存消耗方面的改进。首先，通过验证真实的指纹数据集，我们将RF指纹模拟为3-D低管级张量，以有效地捕获多维潜在结构。其次，我们提出了一种新颖的张甘甘，是一个在回归，发电机和鉴别符之间的三位玩家游戏。我们设计了一种带有输卵管的张量完成算法，作为产生新的RF指纹作为训练样本的发电机，并且回归估计RF指纹的位置。最后，在真实的指纹数据集上，我们表明所提出的张量-GaN方案将本地化精度从0.42米（最先进的方法KNN，DeepFi和AutoEncoder）提高到0.19米，占1639的80％测试点。此外，我们实施了一个原型Tensor-GaN，它被下载为Android智能手机应用程序，该应用程序具有相对较小的内存占用空间，即57 kB。

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