Wifi fingerprinting localization for intelligent vehicles in car park

Nguyen, Van‐Dinh; Charette, Raoul de; Nashashibi, Fawzi; Dao, Thiên-My; Castelli, Eric

doi:10.1109/ipin.2018.8626578

Cited by 6 publications

(3 citation statements)

References 14 publications

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“…Though it is not common for indoor positioning systems to achieve sub-metre level accuracy only using WiFi RSS, certain systems could still get very astonishing results by carefully modified models, for example Belmonte-Hernández et al (2019), Own et al (2019), G. Zhang et al (2019, Xue et al (2020), Hoang et al (2019, Soro and Lee (2019) and D. V. Nguyen et al (2018). The results of these systems are summarised in Table 3.…”

Section: Performance Comparisons Of Systems Employing Deep Learning As a Feature Extraction Methodsmentioning

confidence: 99%

A survey of deep learning approaches for WiFi-based indoor positioning

Feng

Nguyen

Luo

2021

Journal of Information and Telecommunication

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One of the most popular approaches for indoor positioning is WiFi fingerprinting, which has been intrinsically tackled as a traditional machine learning problem since the beginning, to achieve a few metres of accuracy on average. In recent years, deep learning has emerged as an alternative approach, with a large number of publications reporting sub-metre positioning accuracy. Therefore, this survey presents a timely, comprehensive review of the most interesting deep learning methods being used for WiFi fingerprinting. In doing so, we aim to identify the most efficient neural networks, under a variety of positioning evaluation metrics for different readers. We will demonstrate that despite the new emerging WiFi signal measures (i.e. CSI and RTT), RSS produces competitive performances under deep learning. We will also show that simple neural networks outperform more complex ones in certain environments.

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Section: Performance Comparisons Of Systems Employing Deep Learning As a Feature Extraction Methodsmentioning

confidence: 99%

A survey of deep learning approaches for WiFi-based indoor positioning

Feng

Nguyen

Luo

2021

Journal of Information and Telecommunication

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“…Therefore, it can be restricted in advanced vehicles positioning based scenarios. However, Several research have been proposed to localize vehicles through Wi-Fi network [65]. As solution to improve Wi-Fi's localization accuracy, the number of anchors should be increasing, this leads to boost up the cost.…”

Section: Wireless Fidelity-based Technologymentioning

confidence: 99%

A survey of ranging techniques for vehicle localization in intelligence transportation system: challenges and opportunities

Bakhuraisa

Aziz

Geok

et al. 2022

IJECE

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<span lang="EN-US">Observing the vehicles movement becomes an urgent necessity due to exponentially increasing numbers of vehicles in the world. However, to this regard, a good deal of research had been presented to estimate the exact physical position of the vehicle. The major challenges faced vehicle localization systems are large coverage areas required, positioning at diverse environments and positioning during a high-speed movement. However, in this paper, the challenges of employing the vehicle localization techniques, which rely on the propagation signal properties, are discussed. Moreover, a comparison between these techniques, in terms of accuracy, responsiveness, scalability, cost, and complexity, is conducted. The presented positioning technologies are classified into five categories: satellite based, radio frequency based, radio waves based, optical based, and sound based. The discussion shows that, both of satellite-based technology and cellular-based technology are emerge solutions to overcome the challenges of vehicle positioning. Satellite-based can provide a high accurate positioning in open outdoor environment, whereas the cellular-based can provide accurate and reliable vehicle localization in urban environment, it can support non-line of sight (NLOS) positioning and provide large coverage and high data transmission. The paper also shows that, the standalone localization technology still has limitations. Therefore, we discussed how the presented techniques are integrated to improve the positioning performance.</span>

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“…With the rise of multi-floor developments, urbanisation, and smart cities increasing the living space and building complexity, indoor localisation and navigation has played a significant role in assisting users to their destination indoors. Its application has been deployed in commercial buildings [1,2], carparks [3], the health service [4], public transport [5,6], etc.…”

Section: Introductionmentioning

confidence: 99%

extendGAN+: Transferable Data Augmentation Framework Using WGAN-GP for Data-Driven Indoor Localisation Model

Yean

Goh

Lee

et al. 2023

Sensors

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For indoor localisation, a challenge in data-driven localisation is to ensure sufficient data to train the prediction model to produce a good accuracy. However, for WiFi-based data collection, human effort is still required to capture a large amount of data as the representation Received Signal Strength (RSS) could easily be affected by obstacles and other factors. In this paper, we propose an extendGAN+ pipeline that leverages up-sampling with the Dirichlet distribution to improve location prediction accuracy with small sample sizes, applies transferred WGAN-GP for synthetic data generation, and ensures data quality with a filtering module. The results highlight the effectiveness of the proposed data augmentation method not only by localisation performance but also showcase the variety of RSS patterns it could produce. Benchmarking against the baseline methods such as fingerprint, random forest, and its base dataset with localisation models, extendGAN+ shows improvements of up to 23.47%, 25.35%, and 18.88% respectively. Furthermore, compared to existing GAN+ methods, it reduces training time by a factor of four due to transfer learning and improves performance by 10.13%.

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Wifi fingerprinting localization for intelligent vehicles in car park

Cited by 6 publications

References 14 publications

A survey of deep learning approaches for WiFi-based indoor positioning

A survey of deep learning approaches for WiFi-based indoor positioning

A survey of ranging techniques for vehicle localization in intelligence transportation system: challenges and opportunities

extendGAN+: Transferable Data Augmentation Framework Using WGAN-GP for Data-Driven Indoor Localisation Model

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