Semi-Supervised Learning with GANs for Device-Free Fingerprinting Indoor Localization

Chen, Kevin; Chang, Ronald Y.

doi:10.1109/globecom42002.2020.9322456

Cited by 26 publications

(7 citation statements)

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“…In particular, generative adversarial networks (GANs) [30] aim at expanding and improving the diversity of a training database in different research fields including indoor localization. In [31], GANs use both labeled and unlabeled data when the former is insufficient in order to share weights with a localization classifier to benefit from useful information contained in the latter. In [32], [33], GANs are used to improve the diversity of the collected database generating fake received signal strength indicators (RSSIs) at known positions already used for data collection.…”

Section: A Related Workmentioning

confidence: 99%

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DNN-Based Indoor Localization Under Limited Dataset Using GANs and Semi-Supervised Learning

2022

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Indoor localization techniques based on supervised learning deliver great performance accuracy while maintaining low online complexity. However, such systems require massive amounts of data for offline training, which necessitates costly measurements. The essence of this paper is twofold with the purpose of providing solutions to missing data of different nature: available unlabeled data and missing unlabeled data. In both cases, we rely on a few labeled available data, which is costly yet insufficient to achieve a high localization accuracy. To address the problem of available unlabeled data, a weighted semisupervised DNN-based indoor localization approach leveraging pseudo-labeling methods in combination with real labeled samples and inexpensive pseudo-labeled samples is proposed in order to boost localization accuracy, while overcoming the high cost of collecting additional labeled data. As for the extreme case of unavailable unlabeled data, we propose an alternative localization system generating fake fingerprints based on generative adversarial networks (GANs) named 'Weighted GAN based indoor localization'. Furthermore, a deep neural network is trained on a mixed dataset containing both real collected and fake produced data samples using a similar weighting technique in order to improve location prediction performance and avoids overfitting. In terms of localization accuracy, our proposed localization approaches outperform conventional supervised localization schemes utilizing the same collection of real labeled samples. For experimental evaluations, we have tested our proposed methods on both simulated data and experimental data from the publicly available UJIndoorLoc database, which is built to test indoor positioning systems relying on Wi-Fi fingerprints. Results based on experimental data provide the localization accuracy increase compared to the classical supervised learning method using the same set of labeled collected data when using the weighted semi-supervised and the weighted-GAN approaches by 10.11 % and 8.53 %, respectively.

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Section: A Related Workmentioning

confidence: 99%

“…in [31], [35], and we do not assume having sufficient collected data in some regions as in [34]. Moreover, we do not use GANs to further diversify signal measurements at known positions, as considered in [32], [33].…”

Section: B Contributionsmentioning

confidence: 99%

DNN-Based Indoor Localization Under Limited Dataset Using GANs and Semi-Supervised Learning

2022

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“…Especially for the work in [12], we cite this publication as our primary reference. Other proposals implemented variational autoencoders (VAE) that utilised received signal strength indicator (RSSI) and channel state information (CSI) [13]- [15].…”

Section: Introductionmentioning

confidence: 99%

Synthesis of a Small Fingerprint Database through a Deep Generative Model for Indoor Localisation

Suroso

Cherntanomwong

Sooraksa

2023

ELEKTRON ELEKTROTECH

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In deep learning (DL), the deep generative model is helpful for data augmentation objectives to tackle the lack of datasets that have a significant impact on learning performance. Data augmentation or synthesis is expected to solve the issue in a small/sparse database. The problem of databasing also exists in the fingerprint-based indoor localisation system. The dense offline fingerprint database must be constructed with the accuracy requirement. However, this will affect the high cost, massive laborious work, and increase the complexity of the system. Therefore, this paper proposes to address these issues by generating synthetic data via a deep generative model. The generative adversarial network (GAN) is selected to generate the synthetic fingerprint database for indoor localisation. Our database consideration consists of power-based parameters, i.e., the received signal strength indicator (RSSI) from Wi-Fi devices obtained from the actual measurement campaign. Some of the literature mainly discusses how GAN works in a vast and complex dataset. Here, we consider applying GAN in a relatively small dataset and for a simple setup. Our results show that by only using the 20 % fraction of actual RSSI data combined with the synthetic RSSI, the accuracy validation performance is slightly higher than when using all actual data usage. Moreover, in only 60 % of actual data usage and in combination with 625 samples of synthetic data, the accuracy performance is improved to 0.73 (1.37 times higher than the use of all actual data, 0.53). Thus, this result proves that the challenges of offline fingerprint databases can be alleviated by data synthesis through GAN by using only a small dataset.

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“…Such networks generate samples with improved diversity and expand the training database in order to ensure a proper design of deep neural networks (DNNs) in different fields including localization. In [32], GANs are used with semi-supervised learning where GANs use both labeled and unlabeled data to share weights with a localization classifier in order to benefit from data contained in unlabeled information when labeled data are not sufficient. The authors in [33] aim to construct an efficient radio map covering free space (e.g, open spaces and corridors) and constrained spaces.…”

Section: Introductionmentioning

confidence: 99%

“…In this paper, unlike [32], we do not assume the availability of unlabeled data to enhance the training of the generative model, and we do not assume having sufficient collected data for particular regions as assumed in [33] for free space. We consider an extreme and realistic case where only a small amount of labeled data is available.…”

Section: Introductionmentioning

confidence: 99%

Indoor Localization Using Data Augmentation via Selective Generative Adversarial Networks

et al. 2021

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Several location-based services require accurate location information in indoor environments. Recently, it has been shown that deep neural network (DNN) based received signal strength indicator (RSSI) fingerprints achieve high localization performance with low online complexity. However, such methods require a very large amount of training data, in order to properly design and optimize the DNN model, which makes the data collection very costly. In this paper, we propose generative adversarial networks for RSSI data augmentation which generate fake RSSI data based on a small set of real collected labeled data. The developed model utilizes semi-supervised learning in order to predict the pseudo-labels of the generated RSSIs. A proper selection of the generated data is proposed in order to cover the entire considered indoor environment, and to reduce the data generation error by only selecting the most realistic fake RSSIs. Extensive numerical experiments show that the proposed data augmentation and selection scheme leads to a localization accuracy improvement of 21.69% for simulated data and 15.36% for experimental data.INDEX TERMS Indoor localization, received signal strength indicator (RSSI), deep neural network (DNN), generative adversarial network (GAN), semi-supervised learning.

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Semi-Supervised Learning with GANs for Device-Free Fingerprinting Indoor Localization

Cited by 26 publications

References 27 publications

DNN-Based Indoor Localization Under Limited Dataset Using GANs and Semi-Supervised Learning

DNN-Based Indoor Localization Under Limited Dataset Using GANs and Semi-Supervised Learning

Synthesis of a Small Fingerprint Database through a Deep Generative Model for Indoor Localisation

Indoor Localization Using Data Augmentation via Selective Generative Adversarial Networks

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